{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics\n",
    "To make a scatter plot in ggplot, the first thing you'll need is a dataset. ggplot likes pandas DataFrames the best, so it is highly recommended that you load your data into pandas before starting to use ggplot.\n",
    "\n",
    "We'll use ggplot's built-in `mtcars` dataset for this set of examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple X/Y Plot\n",
    "The simplest plot you can make is an x/y plot. To do this in ggplot, first create a base layer using the `ggplot` function. Pass in your data and aesthetics that map columns in your DataFrame to `x` and `y`. In this case, we're going to look at the relationship between car weight (`wt`) and miles per gallon (`mpg`), so we'll set the x value of our aesthetics to `wt` and the y value to `mpg`.\n",
    "\n",
    "Once the aesthetics are defined, we just need to add a `geom_point` layer to our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2Nra4ZhsA4PUo2/AW1ev1DA4OWqcNALwpy0gAAKAgyjYAABRE2QYAgIIo2wAAUJC2+ILk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff73a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285176705)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = ggplot(mtcars, aes(x='wt', y='mpg'))\n",
    "p + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Controlling other aesthetics\n",
    "In addition to the x and y variables, you can also control the shape, size, color, alpha (see throughness), and other \"aesthetics\" of your scatterplot. To do this, just include definitions for the aesthetic you'd like to control. For instance, let's set the color of each point in our graph to the acceleration (`qsec`) of each car."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jxw499thj+pd/+Rd973vfO60QnUlNTY0jL5rv5AvmFxUVOWZu6XRWWWXl9fx1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\nx+9W9wh3XwSAroqlIABwCmr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101a6810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (285320953)>\n"
     ]
    }
   ],
   "source": [
    "p = ggplot(mtcars, aes(x='wt', y='mpg', color='qsec')) + geom_point()\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete Color\n",
    "Since `qsec` is a continuous variable you probably noticed that you got that nice, graduated legend on the right side of the last plot that indicated the value of each color. If you were to use a discrete variable such as `name` for the color, watch what happens.\n",
    "\n",
    "As you can see, each name is assigned its own color and displayed in the legend. This can get a bit unruly (especially if you're plotting a discrete value with hundreds of possible values) so be careful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EVqvVViTAxZWg4WJPw+DBg2nZsiVJSUkAvPPOO4SFhdG2bVs2bdpUYvzFXTMm\nJoaRI0fSvXt3+vfvf90xx8TE8Pzzz9OjRw/+/PPPG3+4qFAQERERESfl4eFhW2V8woQJrFu3js2b\nNzN79mzy8vL4xz/+weLFi1m9ejXZ2dkArFq1ivvuu4/4+Hhatmxpa2vy5MkkJCTYzrdarbZ9aWlp\nBAQEFBvDn3/+yZIlS4iNjWX27NkAPP/88yQmJrJ+/XomT55cYvwlXbNz587Ex8fj5eXFvn37rjvm\n4OBg4uLiqF69+o08VhsNPRIRERERp5Sfn4+X18UvJ5rNZqpWrQpAYGAgaWlpnD59msDAQADatGkD\nwOHDhwkOvjhcKjg4mO+//5709HQOHjxI9+7dsVqtZGZmkp6ejr+/PwABAQGcOHGi2BiaN2+OwWCg\nXr16nD17FoCFCxeyePFijEYjp06dKva8kq4J0Lp1awDq1q1LRkbGdcUMEBISchNP9f+oUBARERER\np3H5X/qnTZtGVFQUAG5ubvz1119UrFiRQ4cOUadOHWrWrMmRI0do2LAhKSkpDBgwgMDAQFJSUoiK\nimL37t0A1KhRgyZNmhAfH4+7uzuFhYW4u//fa3LdunVxd3dn69atReYo1K9fv9jYPvjgA/bs2UN6\nerptiNKVrrym2WzGzc0NKDqHwWq1lirmy883Gh0zaEiFgoiIiIjcsNJMQHakzZs3061bN8xmM+3b\nt+eNN94AYMqUKTz44IMYjUZeeOEFvLy8eOONN3jssceoWbMm1apVA6Bfv34sXbqUyMhIGjZsiIeH\nBwaDgYkTJxIREYHRaMTf35+lS5cWue6iRYv429/+xsSJEzGbzTz33HPUr1+/2InJoaGhhIaG0r59\neypWrAjYT2Au6ZqXH3fp5+uJ2ZETpQ3Wy8syIT093TbWzZX4+PiQm5tb3mE4nIeHB35+fi6bN1Du\nnJly57yUO+fl6rkTx7jUY/Dxxx9z9uxZp/hcaXnErB4FEREREbmj9O3bl+zsbLy9ve2+cHS7Ko+Y\nVSiIiIiIyB1l7dq15R3CdSuPmPV5VBERERERsaNCQURERERE7KhQEBEREREROyoURERERETEjgoF\nEREREXEKqamp+Pv7ExERgclkYsyYMeTl5V1XGx9//PENXXvPnj107tyZsLAw+vbta/sM7x9//EHf\nvn3p1q0bkyZNAuD06dP06NGDLl268MUXX9zQ9W4HWkfhCq76bWlX/660q+YNlDtnptw5L+XOebl6\n7m5HixfHPDavAAAgAElEQVS2cVhb0cNSrro/NTWVMWPGsGzZMgBef/118vLymD59eqmvERISwo4d\nO0p1rNVqtS1gdvnKx5MmTaJRo0ZER0cTHR3Nu+++S+3atW3nvfTSSzz88MN07dqV0NBQkpKS8PT0\nLHWMtwv1KIgUw2I1cybrIL//J5kzWQexWi3lHZKIiIhc4bXXXuPrr78GICwsjPPnzwMwcOBAjh07\nxpo1a2jfvj3dunVj7ty5fPTRRxw4cIDw8HCSkpLYtWsX4eHhdO3alZkzZwIXi4CYmBgeeugh9u7d\na7vWpSIBIDc3l/vuu4/CwkKOHj3KSy+9REREBD/88AMAP/74I2FhYRiNRkJCQti3b19ZPRKH0joK\nl8nLy8PDwwN3d9d7LEajER8fn/IOw+EMBgPnz593eN5OnNnLup/HYrEWYjS407v1O9St0cJh7V8P\n5c55KXfOS7lzXq6cO7Hn4eHBhQsX7LZfel5fffUVCxcuJCgoyLZv/vz5JCQkABAZGcnKlSupXLky\nffr04YknngCgfv36LFiwwK7duLg4Xn31Vby8vHj11Vc5c+YMP/30E8uXL8fd3Z2HH36YH3/8sUiP\nXaVKlfjrr78cet9lxTX/L3GDvL29ycrKcsnuWFfuiq1SpQo5OTkOzdu5nD+wWAsBsFgLOZfzB9Ur\nBDqs/euh3Dkv5c55KXfOy5VzJ/by8/Px8vICihZTFsvFkQCvvfYaM2bMIC8vj1GjRtG+fXsuH3W/\nZ88eoqKisFqtnDt3juPHjwMXhycVp0ePHvTo0YMZM2Ywd+5cRo8eTaNGjahTpw4Anp6emM3mIvk6\nd+4c1apVc+yNlxENPRIpRiWf2hgNF+too8GdSj61r3GGiIiIlIXLX/SnTZtGVFQUANWqVePEiRMU\nFhbyyy+/AFC3bl3mzp3LtGnTmDhxIlC0oGjVqhWrV68mMTGRXbt20abNxfkWRqP9K/LlPReVK1fm\nrrvuwtvbm+rVq5OZmUlOTg75+fm4ubnRrl07EhMTKSwsJCUlhWbNmjn+QZQB9SiIFKO6byC97p9O\nVt5JKvnUprpv+fQmiIiI3O6uNQHZ0TZv3ky3bt0wm820b9+eN954A4CRI0cyYMAAmjdvTq1atYCL\n8w2+//57CgoKGD16NAD33XcfAwcO5KWXXrIVGhaLBW9vb1auXFniMK8NGzYwc+ZMjEYj1atX5/PP\nPwdgypQpPPTQQxQUFNi+ejR27FiGDh3Ka6+9xnPPPWfr9XA2+urRFVz1SxCu3BWrL3g4J+XOeSl3\nzku5c16381ePxHVp6JGIiIiIiNhRoSAiIiIiInZUKIiIiIiIiB0VCiIiIiIiYkeFgoiIiIiI2FGh\nICIiIiIidlQoiIiIiIhTSE1Nxd/fn/DwcMLDw0lKSrol11m4cCHbt2+329a4cWMiIiLo2rUr8+bN\ns+0bOXLkLYmjvGnBNRERERG5YR990cZhbT035NqLt5lMJpYtW1bqNq1Wa4mLqJV0/LBhw4rd9/e/\n/51Ro0aRm5tLVFQU9erVo1evXsyZM6fU7TsTFQoiIiIi4jSuXCs4Pz+fZ555hpMnT+Lr68uiRYv4\n888/GTp0KAEBAbRq1YrffvuNChUqcOjQIRYtWkR0dDSFhYV4enry1Vdf4evrS7NmzejQoQOVK1em\ncuXKhISE8OCDDxYbg4+PD+PGjeOLL76gV69ehISEsGPHDmJiYvD29ubIkSP4+vqyYsUKLBYLw4YN\n4/jx41SsWJFFixZRuXLlsnhUN01Dj0RERETEaSQnJ9uGHp09e5ZPPvmEbt26sXHjRqKjo5k7dy4A\naWlpLFq0iHHjxgEQHBxMXFwcfn5+fP311yQmJtKrVy9iY2MB+OOPP5g1axYzZ84sVRwBAQGcPHkS\noEiPRefOnYmPj8fLy4t9+/axcuVK6tWrR1JSEoMHD+b999935OO4pdSjICIiIiJO48qhR/v372fn\nzp18/vnnFBQU0KVLFwBatmyJm5ub7biQkBAAcnJyGDFiBCdOnCAjI4MBAwYAEBgYSKVKlUodR1pa\nGgEBAXbbW7duDUDdunXJyMjg8OHDtmu3bduW+Pj467zj8qNCQUREREScxpVDj5o0aUKnTp0YMmQI\nAGazmRMnTtjNSzAaLw6kiYuLo2HDhixatIiZM2eSnZ0NUKp5DJeunZuby4wZM3jxxRftYrq8HavV\nSmBgINu3bycqKoodO3bQqFGj673lcqNCQURERERuWGkmIDvSlS/0w4cP59lnn2X+/PkYDAZefvll\nmjZtWuS4y3/u0KEDb731Frt376ZmzZrUr1/f7piSiob333+flStXUlBQwNChQ4mMjCxyfHFt9OvX\njxUrVtC1a1fbHAVnYbBeWZbd4dLT0ykoKCjvMBzOx8eH3Nzc8g7D4Tw8PPDz83PZvIFy58yUO+el\n3DkvV8+dSFnSZGYREREREbGjQkFEREREROyoUBARERERETsqFERERERExI4KBRERERERsaNCQURE\nRERE7KhQuAOYrVb2/ZnOljOnOZSdieUaX8Q1Wy0czPmDzX/u42DOH9c8XkRERKQspKam4u/vT3h4\nOOHh4SQlJREXF8fq1atLPOfjjz8udvuwYcPw9/fnww8/tG1744036NixI506deKLL74ALi6aFhMT\nwwMPPMADDzzAwYMHHXtTtzEtuHYH+D0niwm/pFBoteJuMPBWszY08i15ifIj508y7rf5FFotuBuM\nvB30FI0r1CnDiEVERMRZvBXbxmFtTXj02ou3mUwmli1bVuo2582bx/Dhw+22v/3223Tr1s22MjPA\n0KFDef311ykoKCA4OJghQ4bw008/ceHCBTZv3sx3333Hu+++y9y5c0t9fWfmEoVCYWEhCxYswGw2\nY7FYaNq0KSaTidzcXJYvX865c+eoUqUKAwcOxNvbu7zDLXOn8nIp/N9egUKrlVN5uVctFE7lZVBo\ntfzv8RZO5WWoUBAREZHbwpVrBS9cuJCcnBxGjRrFkCFDSEtLw2w2s3jxYnbu3MmBAwcIDw/n2Wef\nZfDgwbbzatWqZdfW3XffDVxc4M7d/eJrct26dW3H/fXXX3fUwncuUSi4u7szbNgwPD09sVgsfPrp\npwQGBvLrr7/SsGFDQkND+e6779iyZYttqe07SS1vH9wNBluPQi1vn2scXxV3g9HWo1DLu2oZRSoi\nIiJydcnJyYSHhwOwYsWKIvs+/fRTvL29WbVqFXPnzmXy5MkEBQWRkJBwXdd47733GDBgAAA1atTA\n3d2doKAg8vPz2bp1q2NuxAm4RKEA4OnpCVzsXbBYLBgMBn777TdiYmIAaNmyJZ999tkdWSjcW6Ei\n05q3JS03h1rePtxboeJVjw+8K4C3g57iVF4GtbyrEnhXQBlFKiIiInJ1JQ09slgsjBkzhr1793L+\n/HlatGgB2PdAXMu3337Ld999x5dffglAfHw8Hh4e/Pbbb6SkpPDSSy+xdOnSm78RJ+AyhYLFYmHe\nvHn89ddftGvXjjp16pCTk4Ovry8AFStWJCcnp5yjLB9Gg4HmNfy4N9e31Mc3rlBHw41ERETktlPS\ni/9PP/3E2bNnSUpKYsWKFXzzzTcAGI1X/3bP5e3t3buXyZMns2HDhiL7q1evDkC1atXIzMy82Vtw\nGi5TKBiNRp577jny8vKIjY3lP//5j90xBoPB9nNmZmaRySsAvr6+tvForsbNzQ0PD4/yDsPhLuXL\nVfMGyp0zU+6cl3LnvFw9d7ej0kxAdqTL3+cuFxQURGpqKj169CAoKMi23WQyERUVRUxMDH369LFt\nnzBhAl9//TVms5nff/+dd999lxdffJGMjAx69+6NwWBg9erVREZG8tlnn2Eymbhw4QIzZ8685fd4\nuzBYr7c/xgkkJyfj4eFBSkoKTz75JL6+vmRlZbFw4UKef/55ABITE0lOTi5yXteuXQkLCyuPkEVE\nREREbisuUSjk5OTg5uaGt7c3BQUF/Otf/yI0NJTU1FR8fHxsk5lzc3NtcxRK6lEwm80UFhaWx23c\nUl5eXuTn55d3GA7n7u5O1apVycjIcMm8gXLnzJQ756XcOS9Xz51IWbp9+7GuQ3Z2NitXrsRqtWK1\nWmnevDmNGzembt26LF++nN27d1O5cmUGDhxoO6dSpUpUqmT/idD09HQKCgrKMvwy4e7u7pL3dUlh\nYaHL3p9y57yUO+el3DkvV8+dSFlyiUKhZs2aPPfcc3bb77rrLoYNG1YOEYmIiIiIOLerTwMXERER\nEZE7kgoFERERERGxo0JBRERERETsqFAQEREREaeQmpqKv78/4eHhhIeHk5SURFxcHKtXry7xnI8/\n/thuW25uLu3atbMtxvu3v/2NtWvXkpmZSfv27alUqRL79+8vtr0lS5bQsWNHwsPD+e233xxzY7cp\nl5jMLCIiIiLl4/kVbRzW1gf9r714m8lkYtmyZaVuc968eQwfPrzINh8fH1555RXeeOMNHn/8cY4d\nO0bv3r0xm82sW7eOMWPGFNuWxWLhnXfeYceOHZw6dYq//e1vrFy5stSxOBv1KIiIiIiI07hyCbCF\nCxfy4YcfAjBkyBDCwsJ44IEHOHHiBKtWreLAgQOEh4ezdOnSIucNGjSIlJQUnn32Wd59913g4sre\n1atXt7vGJWfOnKFu3boYjUYCAgLUoyAiIiIicrtITk4mPDwcgBUrVhTZ9+mnn+Lt7c2qVauYO3cu\nkydPJigoiISEhGLbeuCBB/jmm29o3Lhxqa7t5+fH8ePHycrK4tixYxw5cgSz2Yybm9vN3dRtSoWC\niIiIiDiNkoYeWSwWxowZw969ezl//jwtWrQA7HsgLjl9+jRxcXG0bNmSdevW8eCDDxZ73K+//sqo\nUaNwd3fn22+/Zdq0afTt25cGDRrQvn17ly0SQIWCiIiIiDiRkl78f/rpJ86ePUtSUhIrVqzgm2++\nAcBoLH6k/auvvsqUKVNo1qwZDz30ED169Cjy0n/pOk2aNCExMdG2vXv37nTv3p1Dhw4xa9YsR93W\nbUmFgoiIiIjcsNJMQHYkg8FQ7PagoCBSU1Pp0aMHQUFBtu0mk4moqChiYmLo06cPADt27CA7O5uu\nXbsCMHjwYGbPns3f//53evfuzc8//8zBgwcZMWIEQ4cOLXKdF198kT179lC9enU++uijW3SXtweD\ntaSy7A6Vnp5OQUFBeYfhcD4+PuTm5pZ3GA7n4eGBn5+fy+YNlDtnptw5L+XOebl67kTKkr56JCIi\nIiIidlQoiIiIiIiIHRUKIiIiIiJiR4WCiIiIiIjYUaEgIiIiIiJ2VCiIiIiIiIgdFQoiIiIi4hRS\nU1Px9/cnPDyc8PBwkpKSrruNgQMHcuzYsSLbYmJiaNeuHZ07d2bUqFHX1d65c+dYvnz5dcdRkuTk\nZMaMGeOw9m6GFlwTERERkRvW/ps2Dmtr+0PXXrzNZDKxbNmyUrdptVpLXKTtcgsXLqRJkyY8+OCD\n/PDDD3To0KFU7Z89e5Zly5YxcODAUsd0LaWJtyyoUBARERERp3HlWsFZWVk8/vjjZGZmUrt2bT7/\n/HO2bt3Ku+++i4eHBw8//DD16tVj7Nix3H333Zw6deqq7bZq1Yrjx49z+PBhPv30U7KysnjxxRcZ\nMmQIkyZN4siRI/z555+cP3+eDRs2MGfOHJKTkwkPD+fDDz8kNTWVKVOmYLFYeOGFF3j00UeJiYmh\nQoUKHDp0iEWLFhEdHU1hYSGenp589dVX+Pr63vLndiM09EhEREREnMall/Lw8HDOnj3LvHnz6N27\nN4mJiTRr1oylS5cCkJmZyVdffcWTTz7JP/7xDxISEliyZAlpaWkltm02m9m2bRtBQUEMGDCAxMRE\nvvvuO2bOnGk7pnHjxqxdu5YOHTrw7bffMnLkSEwmEwkJCQQFBTF58mQSEhLYvHkzs2fPthUgwcHB\nxMXF4efnx9dff01iYiK9evUiNjb21j6wm6AeBRERERFxGlcOPTp8+DDPPvssAG3btmXbtm3Uq1eP\ntm3b2o4xm81UrlwZgBYtWhTb7pNPPkmFChV46KGHaNGiBStXruT999/HarVy5MgR23GtW7cGoG7d\numRkZBRpIz09nYMHD9K9e3esViuZmZmkp6cDEBISAkBOTg4jRozgxIkTZGRkMGDAgJt9JLeMCgUR\nERERcRpXDj1q1KgR27dvp3Xr1uzYsYNGjRoBYDT+38AZd3d3zp07h7e3N3v37i223c8++4ymTZva\nfp8yZQpbtmwB4N5777Vtv3z+gNVqxcPDg8LCQgBq1KhBkyZNiI+Px93dHbPZjJubW5F44uLiaNiw\nIYsWLWLmzJlkZ2df8x7LiwoFEREREblhpZmA7EhXTvR95plnGDJkCLGxsdSsWZPx48ezdevWIsdM\nmjSJ8PBw7rnnHho0aHDNNgEeeeQRunTpQuvWralWrVqJ8dSuXZvc3FwGDRrE1KlT+cc//kFERARG\noxF/f3+WLl1apP0OHTrw1ltvsXv3bmrWrEn9+vXt2vzyyy/5+eefAXj88ccZOnTo1R/KLWKw3i4l\ny20iPT2dgoKC8g7D4Xx8fMjNzS3vMBzOw8MDPz8/l80bKHfOTLlzXsqd83L13ImUJU1mFhERERER\nOyoURERERETEjgoFERERERGxozkKl8nLyyMvL++2mWnuSEajEYvFUt5hXDezpZBjGb+SkXuSqj61\nqV+1KW5GN9t+g8GAp6cnFy5ccMm8gfPm7lqUO+el3Dkv5c55GQwGqlSpUt5hyB1GXz26jLe3N1lZ\nWS45wctZJ3elZR/k81/GYrEWYjS4M7TZdAJ8G9v2e3h4UKVKFXJyclwyb+C8ubsW5c55KXfOS7lz\nXh4eHuUdgtyBNPRIbmsZeSexWC9+m9hiLSQj72Q5RyQiIiJyZ1ChILe1qt61MRoudnwZDe5U9a5d\nzhGJiIhIeUlNTWXgwIG239euXcukSZNuuL3rOX/btm2EhYVhMpmIiIggJSWFuLg4Vq9eXeI5I0eO\nvOHYbgcaeiS3tdoVAhnabDoZeSep6l2b2hUCyzskERERuUzwmhEOa2tXn7nXPObKxdGKWyztepTm\n/IyMDEaNGkV8fDz+/v5kZWVx5MgRevTocdXz5syZc1OxlTcVCnJbMxiMBPg2LjIvQURERO5cJU3E\nX7p0Ke+99x5Go5FJkyYRGRlJWFgYbdq0YefOndx///3Mnj2bzMxMHn30UQwGA5UrV6ZJkyYADBky\nhLS0NMxmM4sXL6Zu3bq2tteuXUtUVBT+/v4AVKxYkVatWrFw4UKys7OpVasWR44cYezYseTk5NCn\nTx82bdpESEgIO3bs4MiRI4wYMQKLxUJwcDAzZsy49Q/KATT0SEREREScRnJyMuHh4YSFhTFhwgQA\nLBYL06ZNY8uWLcTFxdm2A/Tv35/k5GRSUlLIysri448/5pFHHmHdunXcfffdtuM+/fRTEhMTeeml\nl/joo4+KXDMtLY2AgIBi4zEYDPTu3Zt169YBsGbNGvr27WvbBzB27FjeeecdEhISnKZIAPUoiIiI\niIgTMZlMLFu2DLj4l/5du3aRnp5O/fr18fDwwMPDA09PT8xmMwCtWrUCoE6dOpw9e5bDhw/z7LPP\nAhASEsK+ffuwWCyMGTOGvXv3kpubS/PmzYtcMyAggEOHDpUYk7e3Nw0aNODQoUN8+eWX/M///E+R\n/cePH7fF4UzUoyAiIiIiTqO4oUd+fn4cO3aMCxcukJmZyYULF3Bzu7ju0qW/6lutVqxWK40aNSIl\nJQWAnTt3AvDTTz9x9uxZkpKSGDdunN01evfuzZo1azh16hQAWVlZ7N69u8gxjz76KPPmzSM3N5da\ntWoV2Ve/fn3b8c60hol6FERERETkhpVmArIjFTf52Gg0Mm7cOLp06YKbmxtTpkyxO/bSz08//TSD\nBg1i+fLl1K5dm3vuuYegoCBSU1Pp0aMHQUFBdu1XrVqVOXPm8Nhjj2G1WnF3d7cbQtS9e3eeeuop\nJk+ebHf+22+/zfDhwwGcao6CVma+Qnp6uksuQuPKC9D4+fm5bN5AuXNmyp3zUu6cl6vnTqQsaeiR\niIiIiIjYUaEgIiIiIiJ2VCiIiIiIiIgdFQoiIiIiImJHhYKIiIiIiNhRoSAiIiIiInZUKMidy2LF\n7YQFjz1m3E5YwKIvBYuIiNzOUlNTGThwoO33tWvXMmnSJIdfZ/Xq1Zw5c6bYfVarlWbNmvHhhx/a\n7YuJiaFdu3ZERETQvXt3tmzZUmwbL730Evn5+Q6N+VbQgmtyx3JLs+L78QUMZrC6QfZwT8x17Rdx\nERERkZKFrP4vh7W1o++1X/qvXHCtuAXYrmS1Wkt13CWrVq0iMDCQGjVq2O1bsmQJDRo0KPHczz77\njKZNm3Ls2DF69uxJUlIS/v7+RWKZOXNmqWMpT+pRkDuW8S8rBvPFnw3mi7+LiIjI7a2ktYKXLl1K\nhw4d6NSpE99++y0AYWFhjBs3jp49e5KcnEyvXr3o378/rVu3Zv/+/QDExcXxwAMPEBoaSmxsLEeP\nHmXDhg089dRTjB8/vsg1LBYLy5cvZ9CgQdeMs379+gwaNIj4+HiSk5Pp06cPjzzyCJ999hlhYWHk\n5OTQr18/Tp8+DcD8+fOZN28e+fn5PPHEE0RERNCvXz+ys7Nv5nHdFBUKcseyVDNgdbv4s9Xt4u8i\nIiJye0tOTiY8PJywsDAmTJgAXHyBnzZtGlu2bCEuLs62HaBnz57ExcUBUFhYyIoVK5g6dSrz588H\nYPLkySQkJLB582Zmz55NgwYN6NWrFwsWLGDatGlFrv3FF18waNCgUvdO1K5dm5MnTwKQmZnJV199\nRUxMDAaDAYPBwKBBg1i2bBkAX331FQMGDOCTTz6hW7dubNy4kejoaObOnXtzD+wmaOiR3LHMAQay\nh3ti/MuKpZoBc4AKBRERkdudyWSyvVyvXbuWXbt2kZ6eTv369fHw8MDDwwNPT0/M5ovDBkJCQmzn\ntmrVCoB69eqRkZFBeno6Bw8epHv37litVjIzM0lPTy+218JisbBs2TJWr17Nv/71rxJ7Ni6XlpZG\n48aNAWjbtq1t+6Vz+/TpQ79+/YiOjsbNzY1q1aqxf/9+du7cyeeff05BQQFdunS5wSd181QoyJ3L\naMBc14C5bnkHIiIiIqVV3Au6n58fx44d48KFC+Tl5XHhwgXc3C4OGzAa/28AzeU9AVarlRo1atCk\nSRPi4+Nxd3fHbDbj5uaGp6cnhYWFRa5x6tQpTp8+zUMPPcSJEyewWCy0b9++SAFweXzHjx9nxYoV\nJCQksH///iJxXOLr60v16tWZOXMmAwYMAKBJkyZ06tSJIUOGANgKnvKgQkFEREREblhpJiA7UnHD\nfoxGI+PGjaNLly64ubkxZcqUEo+9sq2JEycSERGB0WjE39+fpUuX0rNnT1588UUiIiJ49dVXAQgI\nCODHH38E4PPPPyc7O9uuSAB46qmnqFixIm5ubsydOxc/P7+r3sOgQYN48sknSUtLA2D48OE8++yz\nzJ8/H4PBwMsvv0yvXr1K+XQcy2AtTb/JHSQ9PZ2CgoLyDsPhfHx8yM3NLe8wHM7DwwM/Pz+XzRso\nd85MuXNeyp3zcvXciZQlTWYWERERERE7KhRERERERMSOS8xROHfuHCtXriQnJweDwUBwcDDt27cn\nKSmJXbt2UaFCBQC6detGo0aNyjlaEREREZHbn0sUCkajkR49elC7dm3y8/OZN28eDRs2BKBjx450\n6tSpnCMUEREREXEuLlEoVKxYkYoVKwLg5eVFjRo1yMrKKueoREREREScl0sUCpfLyMjg1KlT1KlT\nh2PHjvHjjz/y888/ExAQQI8ePfD29i7vEEVEREREbnsuVSjk5+ezbNkyevXqhZeXFyEhIXTt2hWD\nwcCmTZuIi4ujb9++wMVltLOzs4uc7+vri7u7Sz0SGzc3Nzw8PMo7DIe7lC9XzRsod85MuXNeyp3z\ncvXc3elSU1N55ZVXWL58OXBxZeadO3fyX//1Xw69zurVq+ncuTM1atQosj02Npb33nsPT09PatSo\nwVdffXXD10hOTqZOnToEBgYWu//Ke71eqampHDx4kMjIyBuO0WX+rTObzSxbtoyWLVsSFBQEYJvE\nDBAcHMzixYttv+/atYvk5OQibXTt2pWwsLCyCVgcqmrVquUdgtwg5c55KXfOS7kTRwpZNcthbe3o\n9+I1j7lyEbVrLaoGF1dLLs1xl6xatYrAwEC7QmHatGns3LkTNzc3zp07V+r2iosnKSmJtm3bllgo\nQOnurSRHjx4lPj5ehQJcrPz8/Pzo0KGDbVtWVpZt7sKvv/6Kv7+/bV9wcDD33XdfkTZ8fX3JyMiw\nW7LbFXh5eZGfn1/eYTicu7s7VatWddm8gXLnzJQ756XcOS9Xz51cfMkuztKlS3nvvfcwGo1MmjSJ\nyMhIwsLCaNeuHT/99BMTJkxg2rRp+Pj48O9//5svvviCpk2bEhcXx5QpU7BYLLzwwgu0b9+eDRs2\nsH//fsLCwpg2bZrtGnn/n717j4uqzh8//jozDMhVLqJom7SAbJppGaQgMNxU1KgURbNV0tp8uKtd\nNhGzrZU1b7hurLtR7WZpm3fDdd36iaLAeKm8YbpamlaooQaCchGFmTm/P8j5NqFJOoAzvp+Ph4+a\nM+d8znvOW2He5/P5nM+lS2zfvp3o6Gjat28PQGZmJkeOHKGyshJFUVizZg3u7u5MnTqVTz/9FBcX\nF9555x26du1Kjx49iIiIQKfTkZeXR25uLmvWrCErK4vRo0djMpno1KkTq1atAuDUqVOkpKRQUlLC\nn//8Z2JjYykoKGDGjBkoisJvf/tbfv3rXzN+/HjS09Pp0aMH6enpPPTQQ7zxxhvs3LmTvXv3kpub\ni7e398++1g5RKJw4cYKDBw/SsWNH3nzzTaDxUagHDx7kzJkzKIqCt7c3ycnJlmO8vLzw8vJq0paj\nrjsju0MAACAASURBVFbp5OTkkJ/rCqPR6LCfT3JnvyR39ktyZ78cPXeicchOfHw8qqpSUVFBSkoK\nZrOZefPmsXv3bi5dukR8fLzlTnpSUhLz58+nqKgIo9FIbm4uGzdu5J133uHPf/4zs2bNorCwEI1G\nQ0xMDKmpqQwePJipU6fSo0cPq3MvW7aMOXPmMGHCBJ544glefvllAIKCgnj11Vf5xz/+wT/+8Q9i\nYmIoLS1l27ZtbN++nczMTBYvXkxpaSmvvfYaXl5e/OlPfyIsLIwhQ4ZgNBrJz89Ho9Hw3HPPsXXr\nVkJCQjh79iwGg4ELFy6QnJzMzp07mTFjBh999BGenp5ERkYycuTIJtdIURQmTZpE165dycrKuuFr\n7RCFQteuXa86Nk3WTBBCCCGEcCyxsbGsXr0aaJyjsHfvXsrKyujatSs6nQ6dToezszMmkwmA8PBw\ny7H33XcfAHfeeSeVlZWUlZVx9OhRBg4ciKqqVFVVUVZWds1eiz59+rB27VqMRiODBg1i1KhRQONI\nFYCwsDDefvtt7rjjDst5w8PDeemllwAICQmx3Kj+4TnKy8uZNGkSlZWVnD59mgceeICQkBB69uyJ\nk5MTfn5+ls9jMpksvUshISGUlpZaDVG6Vuw3QlZmFkIIIYQQduNqX4T9/f05ceIE9fX1VFVVUV9f\nj1arBRrX27rix1+oO3ToQPfu3dm0aRMFBQUUFxfTsWNHnJ2drzo879ixY0Bjz5W3t7clluLiYgD2\n7NlDt27dCA4OZteuXQDs2rXLcvP6h+fX6XSWL//Lly8nOTmZwsJCBg0aZGn3f//7H0ajkYqKCsuE\ndq1WS0VFBQ0NDXz55Zd06dIFHx8fTp06BcCBAwcs7d/sEEOH6FEQQgizqvJVNZytg06uEOQJmpuY\nBCaEEKJ5mjMB2ZauNsFXo9GQkZFBdHQ0Wq2W2bNnX3PfH7f10ksvkZiYiEajoWPHjqxcuZKkpCSe\nf/55EhMTefHFFy37T506lfLycjQaDdHR0Zb5ridPnmTQoEFoNBrWrFmDh4cHnTt3Jjo6Gp1Ox7vv\nvtsknvj4eDIyMti6dSvjx4/n17/+NRs2bMDV1dWyz5133sljjz3G119/zYIFCwCYPXs2Q4YMQaPR\nMGXKFFxcXHjiiScYO3Ysb731Fm5ubgDce++9vPjii4waNYp//vOfVx1yf91rrdqyf8IBOOocBVdX\nV+rq6to6DJvT6XT4+/s7bN5Actdcx6pUXt4HJhW0CszqAyFebVsoSO7sl+TOfjl67sStJzMzk/Dw\ncIYMGdLWodicDD0SQjiEs3WNRQI0/ves431PEEIIcQu6mUeY3upk6JEQwiF0cm3sSbjSo9DJ9frH\nCCGEEDfrlVdeaesQWowUCkIIhxDk2Tjc6IdzFIQQQghx46RQEEI4BI2iEOIFIT9/rpYQQgghrkLm\nKAghhBBCCCGakEJBCCGEEEII0YQUCkIIIYQQwi6UlJQwcuRIq21ZWVmUlJTYvN1rqa+v5/nnnycm\nJga9Xs8LL7zws8712WefsXv3bgDOnj1LZmbmz473apYuXfqTC6wtXbqUnJycn9WmzFEQQgghhBA3\n7MF1/7RZW7uG/ea6+/z4caTTpk2zybmb+5jT2bNn4+/vj8FgAKCwsPBnnWf//v3U1NQQHh5Op06d\n+OMf//hzQ72qJUuWMGLECMsKzrYgPQpCCCGEEMJujR8/nsOHD1NUVMTgwYMZPnw4999/P4cPHwbg\no48+IiIigvj4eJYtW4bZbGbs2LHExsaSnJzMhQsXrNqLi4vjhRdeQK/X88wzzzQ535o1a0hPT7e8\njo2NBWDlypX069ePyMhINm/efM223njjDRYtWkRSUpJVT8YP950yZQoAly9fZuzYsSQmJvLoo49S\nU1NDSUkJUVFRjB49mt69e1NYWMgnn3zC/v37GTJkCNnZ2eTn5xMbG0vfvn3Jysq64WsrhYIQQggh\nhHAIRqOR3Nxc5s6dyzvvvIOqqsyYMYP8/Hy2bt3K448/zrp167jzzjspLCxk9OjRLFq0qEk7w4cP\np6ioiD179lBdXW31Xn19PTqdzmqb2Wxm3rx5bNu2jby8PGbMmNGkrb1791JdXc2kSZN49tln2bhx\nI2Ddk3Fl33379lFdXc3bb79NQkIC+fn5jBkzhrfeeguAc+fOsWLFClatWsXf//53+vXrx/3338/G\njRt57rnniIqKshQQa9eu5fLlyzd0PWXokRBCCCGEcAj33XcfAHfeeSeVlZWUlZVx55134u7ubtnn\n2LFjhIeHAxAWFsamTZuu2c4vfvELzp8/j6fn/y3O4+zsTENDg1WxUFZWRmBgIDqdDp1Oh7OzMyaT\nyaqtO+64g/Pnzzcr/iv7Hj58mD179vDee+/R0NBAdHQ0AD179kRRFMvnBFBVFVVVAdizZw+ZmZk0\nNDRQUlLCd99919xLaEV6FIQQQgghhN248mX4an54d15VVfz9/fn222+pra21bAsJCeHTTz8FYPfu\n3XTr1u2a7fzwy/cVqampLFiwwPK6qKgIf39/SkpKqK+vp6qqivr6erRa7VXb0ul015x0/ON9u3fv\nzjPPPMPWrVvZtm0bs2bNuub10Ol0luIkKyuLt956i4KCArp06fKT1+ynSI+CEEIIIYS4Yc2ZgGxL\n27dvZ+DAgQAkJCT85CRkRVF49dVXSUhIwN3dnQkTJjB69Ghyc3PR6/V4enry/vvvW81T+GF7V2t7\nxowZTJs2jZiYGBRFITw8HL1eT0ZGBtHR0Wi1WmbPnn3NtiIiIhg3bhy7du2y7HetfX/zm9/w9NNP\n884776AoCi+88AI9evS4alwPP/wwqamppKSkMGLECB599FHuvfdevLxufCVSRb3REsNBlZWV0dDQ\n0NZh2Jyrqyt1dXVtHYbN6XQ6/P39HTZvILmzZ5I7+yW5s1+OnjshWpMMPRJCCCGEEEI0IYWCEEII\nIYQQogkpFIQQQgghhBBNSKEghBBCCCGEaEIKBSGEEEIIIUQT8nhUIWxEVU1cPn+chtoz6NwDcPEO\nQVGkFm8pJlXl6xojZ+pMBLhqCfJwQvMTj8gTQgghxM8jhYIQNnL5/HFObssA1QiKE3dGz6edT2hb\nh+Wwvq4x8of9FzCq4KTArN7t6ealu/6BQggh7FZJSQlTp05lzZo1P7lfUVERd9xxByEhITd0nri4\nOMxmMxqNBkVRWL9+vdXqzLcLKRSEsJGG2jONRQKAaqSh9owUCi3oTJ0J4/erwBhVOHvJJIWCEEK0\ngb65y2zW1qfDH7/uPj+1wNoVhYWFhIWF3XChoCgKGzduxNXVtdnHqKrarNjsiYyLEMJGdO4BoHxf\neytOja9Fiwlw1eL0/c9jJ6XxtRBCiNvP//73P6Kjo4mOjmb+/PlcunSJJUuWMGPGDJ544gmKiopI\nT08H4NChQ4wfPx6j0cjDDz9MfHw88fHx1NfXW7Wpqipms9lq2/jx4zl8+DAA6enpGAwGioqKePjh\nh0lJSWHp0qUUFhYSERFBZGQk77//vuW4p556igEDBvDYY4+hqirfffcd8fHx6PV6UlNTuVXXP5Ye\nBSFsxMU7hDuj51vNURAtJ8jDiVm923P20v/NURBCCHH7mTFjBosXLyY0NJSkpCQee+wxxo8fT1hY\nGEOGDKGoqMjqTr+iKJw4cQJ3d3f+85//XLPdIUOGoNFocHNz48MPP7zmflVVVRQWFgIQERHBRx99\nhKenJ5GRkYwcORKAfv368fbbbzNjxgzWr1/PQw89RH5+PhqNhueee46tW7eSkJBgmwtiQ/Kb9Qcu\nXbqETqfDycnxLotGo/lZ3Wf2QlEULl68eMvkzc2tN9Dbpm1K7q6tl5uNg7IxyZ39ktzZL0fOnbi6\nM2fOEBraONT3/vvv5/jx41Z36H947a5sDwoKIjIykrFjx3LXXXfxpz/9qUkx8eOhR1drByAsLMzy\n/yaTCR8fHwBCQkIoLS0F4IEHHrDs++WXX1JeXs6kSZOorKzk9OnTlvdvNY75U+IGtWvXjurqahoa\nGto6FJtzdXWlrq6urcOwOZ1Oh7e3N7W1tQ6ZN5Dc2TPJnf2S3NkvR86daPTjYToBAQEcOXKE0NBQ\n9u3bx6RJk/j4448xGhvnDfr4+HDy5EkAPvvsMwAaGhqYPHkyiqIwceJEduzYQVRUlNU5fjz0yNfX\nl1OnTtGjRw8OHDjAww8/DDQWp1dotVoqKirw9PTkyy+/pEuXLgAUFxdz//33s2fPHsLDw1m+fDnJ\nyclMmDCBZ555xv6HHt15551XrWZdXFz4xS9+wfDhw5k0aZLD3qEQQgghhBBNNWcCsi1t376dgQMH\nApCYmMjs2bN58sknARg6dChdu3YlPj6ejIwMCgoKeO2117h48SIDBw7knnvuAeCbb77hySefRKvV\n4uHhQZ8+fazOoSiKZeiRoii89957pKWlMXbsWN566y3c3K7epT1nzhzLcVOmTMHFxQWAvXv3snz5\ncjp06MCrr77KwYMHGTt2LBs2bLile8AUtZklzIIFC3j//fd55plnuPPOOzlx4gSvv/46I0eOxNfX\nl4ULFzJs2DCysrJaOuYWVVZW5pB3WRz5Dou/v7/D5g0kd/ZMcme/JHf2y9FzJ+zP+PHjSU9Pp0eP\nHm0dys/W7Nv/S5YsYfPmzZYuFIDBgwczcOBADh06RFxcHImJiXZfKAghhBBCCGEr9jy/pNmFwunT\np/Hw8LDa5u7ubpmkERoayvnz520bnRBCCCGEEHbsnXfeaesQbliz11FITk7mkUceIT8/ny+++IL8\n/HxSUlJITk4G4OOPP+auu+5qqTiFEEIIIYQQrajZhcJbb71F3759mThxIvfffz9PP/004eHhvPnm\nm0DjY6Z+6hmzQgghhBBCCPvR7KFH7dq1Y968ecybN++q7wcEyCq0QgghhBBCOIqf9SzTrVu3smLF\nCkpLS+nSpQujR4++JVeRE0IIIYQQQtycZg89WrhwIaNHj8bX15ehQ4fi5+fHmDFjWLhwYUvGJ4QQ\nQgghBAAlJSVoNBqKioqAxoXTfH19ycnJafFzr1+/nvLycgDy8vJYv3691ftFRUWWNRwiIiIsMdqz\nZvco/OUvf2Hr1q307NnTsm3s2LEMGDCAF154oUWCE+K2YlbRnrmIpvIyZh8XTJ3dwI4fqSaEEOL2\n0PeDdTZr69OUYdfdJywsjNzcXPR6Pfn5+YSGhtrs/NC4KvPVHmn673//m5CQEDp06MCgQYOueuzo\n0aPJysri9OnTjBs3Dr1ef8PnuxX8rKFHISEhVq+DgoJu2Q8mhL3RnrmI59IvUcwqqkahOq0bpi7u\nbROM2Yz2bAWa89WYvT0xBfjZVdFiUs18VVvOmctVBLh4Eezuj8aO4hdCCHFtgYGBnDhxAoB169Yx\nfPhwy3uPP/44paWlmEwmli9fTkBAAMOHD6empgaAjRs3Ul1dzVNPPUV1dTWdO3fmvffew2AwsHDh\nQnQ6HcnJyZSXl/Phhx9SXV3N/PnzCQ4OZuPGjRw+fJi4uDi6d+9OTU0Nv/vd76xiu7KO8YULF2jf\nvj0A3377LU888QQNDQ306tWLRYsWsXTpUjZu3MjFixeZNGkSmzdvZu/evVy6dIl//OMf9OrVqzUu\n5XU1u1CYOXMmTz75JDNnzuQXv/gFJ0+eZNasWWRmZmI2my37aTTNHs0khPgBTeVlFHPjDxjFrKKp\nvNxmhYL2bAVeK/JQzGZUjYaqxwZh6tyhTWK5EV/VljP9i/UYVTNOioa5dz9CqEfHtg5LCCGEjURE\nRGAwGCgvLycqKspSCCxevJh27drx73//mzfffJMJEybg7u7Of/7zH8ux8+bN49lnnyU2NpasrCxy\nc3Pp0KEDVVVVFBYWAnDp0iWmTp1KWVkZI0eOpLCwkKSkJMsKy0uXLr3qzfJVq1axZ88evvjiC95+\n+23L+aZNm8aAAQP4zW9+w/bt2wFwdnZmxYoVAMTGxtKuXTv2799PVlYW77//fktevmZrdqEwceJE\nAMsHumLZsmVMnDjR0m1iMplsG6EQtwmzjwuqRrH0KJh9XNosFs35apTvbwAoZjOa89V2VSicuVyF\nUW2M36iaOXu5SgoFIYRwEIqikJKSQmpqKmlpaZa7+GazmfT0dA4ePEhdXR09e/YkKCiIyMhIxo4d\ny1133UVmZiaHDx9m165daLVa6urqGDt2LB06dCAsLMxyjqVLl7J8+XI0Gg1nzpxpdmxXhh7V1dXR\nr18/EhMTOXbsmKXtsLAwvvzySzQaDeHh4ZbjsrKy2LJlC6qqotPpbHSlbl6zC4Wvv/66JeMQ4rZn\n6uxGdVo36zkKbcTs7Ymq0Vh6FMzenm0Wy40IcPHCSdFYehQCXLzaOiQhhBA2FBwcTHR0NCNGjGDz\n5s0A7N+/n/Pnz1NYWEhubi7//e9/aWhoYPLkySiKwsSJE9m5cyfdu3dn2LBh9O/fHwCTycT27dut\nRsX8/e9/58CBA5SVlREdHQ2ATqe77g3xK0WLi4sL9fX11NfX061bNz799FOSkpLYvXs3TzzxBMeP\nH7ecr6Kigs2bN7Nt2zb27dvH1KlTbX69blSzCwVvb28WLVpEcXGxpXvnik2bNtk8MCFuO4qCqYt7\n281L+AFTgB9Vjw2ynqNgR4Ld/Zl79yOc/cEcBSGEEC2jOROQW0J2drbV67vvvpuSkhIGDRrE3Xff\nDcA333zDk08+iVarxcPDgz59+tCjRw9+85vf8Morr6AoCllZWU3ajo6OJioqir59++Lh4QHA4MGD\nee6550hMTOSOO+64akyrV69m7969XLx4kQkTJuDp6cm0adNIS0tj7ty59OzZk6ioKI4fP245xsfH\nBz8/P+Lj4+nbt6+tLo9NKOqV0uc6Bg4ciMlkYtiwYbi6ulq99+STT7ZIcG2hrKyMhoaGtg7D5lxd\nXamrq2vrMGxOp9Ph7+/vsHkDyZ09k9zZL8md/XL03AnRmprdo/DJJ59QXl6Os7NzS8YjhLgJJlXl\nq5o6zl6qp1M7Z4I9XFv8aT8mVeXrmsucqTMS4OpEkIeLPGFICCGEcADNLhSioqL44osvbpnHNQkh\nmvqqpo6X/vcVRlXFSVGY3TOIbp4tO9fh65rLvHSgFKMKTgq82qsL3Tzbteg5hRBCCNHyml0oLFmy\nhCFDhtC3b186depk9d4rr7xi88CEED/f2Uv1GL8fTWhUVc5eqm/xQuFMnRHj9wMYjSqcrTPSzb7m\nPgshhBDiKppdKLz00kucPHmSu+66i6qqKst2WXBNiFtHp3bOOCmKpUehU7uWHyoY4OqEk4KlRyHA\n9Wet4yiEEEKIW1Szf6OvXLmSo0eP0rlz55aMRwhxE4I9XJndM8hqjkJLC/Jw4dVeXTj7gzkKQggh\nhLB/zS4UgoKCbqkFIIQQTWkUhW6ebi0+3KjpOdvJcCMhhBDCwWiuv0ujsWPH8vDDD7NixQq2bt1q\n9UcIIYQQQoiWVlJSgkajoaioCICGhgZ8fX3Jycm5ofZef/113nvvvevuV1RURNeuXYmPjycyMpLi\n4mKgcWG2BQsWWGLT6/WWY1RV5Z577rnh2G4Fze5ReP311wGYMWOG1XZFUfjqq69sG5UQQgghhLAL\n/dbm2aytT0YMuu4+YWFh5Obmotfryc/PJzQ01Gbn/ymjR48mKyuLHTt2MHfuXFavXs2kSZPQ6/WM\nGzeOadOmWYoGgBUrVhAYGNgqsbWUZhcKX3/9dUvGIYQQQgghxHUFBgZy4sQJANatW8fw4cMt7z3+\n+OOUlpZiMplYvnw5Go2Gxx9/HEVR+Prrr5k6dSqPPPIIY8aMwdPTE2dnZ4YNG4aqqgwcOBCj0Yiz\nszMffPCBZUXmK66sUVxZWWnZptVqmTVrFo8++iihoaE8+OCDAJjNZtasWUNqaioXL15s6UvSYpo9\n9EgIIYQQQohbQUREBAaDgfLycgICAizbFy9eTEFBAb///e9588036dKlCwUFBSxZsoTg4GDS0tKY\nP38+f/zjH/nwww9p165x3R9FUdiwYQMFBQUMHjyYVatWNTnnqlWriIyMZNy4cbz88suW7eHh4Rw5\ncoTRo0dbti1btozU1FS7fzqoQzzH8MKFC6xbt47a2loURaFPnz7069ePuro61qxZw4ULF/D29mbk\nyJGWvxBCCCGEEML+KIpCSkoKqamppKWlWe70m81m0tPTOXjwIHV1dfTs2ROAmpoaJkyYwDvvvIOH\nhwfHjh2jT58+QOOXfIDa2lomTpzIqVOnqKysZMSIEU3Oe2Xo0fz58/n444+59957AZg1axbTp08n\nKyuLwYMHYzabWb16NevXr+df//qXJT575BA9ChqNhkGDBvG73/2OJ598kt27d1NWVsb27dsJCgpi\nypQp/PKXv2Tbtm1tHaoQQgghhLhJwcHBREdHW32h379/P+fPn6ewsJCMjAxUVUVVVSZMmMDMmTPp\n2rUrAN26dWPfvn0A7NmzB4C8vDyCgoIoLCy0Kj6u5rnnniMnJwez2czRo0fZv38/06ZNIyoqin/9\n61+cOXOGs2fP8tBDD7Fw4ULeeOMNy3nsjUP0KHh6euLp2fhsRhcXFzp06EBVVRVffPEF48ePB6B3\n794sWbKEAQMGtGWoQgghhBAOpTkTkFtCdna21eu7776bkpISBg0axN133w3Ajh07KCwspLy8HIDJ\nkyeTnp7OmDFjWLhwIV5eXgD069ePOXPmUFxcTKdOnSxFxdW4uLiQlJTEmjVrWLFihWUC8/Tp04mP\njyclJYVdu3YB8N5771FTU0NYWJjNP39rUFR77g+5isrKSpYsWcJvf/tbXnvtNaZPn255b968eZbX\nVVVV1NTUWB3r4eGByWTCaDS2asytwcXFhcuXL7d1GDbn5OSEj48PlZWVDpk3kNzZM8md/ZLc2S9H\nz50QrckhehSuuHz5MqtXr2bw4MG4uDRdHfaHE0r27t1reQbvFXq9nri4uBaPU9ie/PC0X5I7+yW5\ns1+SOyFEczhMoWAymVi9ejW9e/e2dDd5eHhQU1ODh4cH1dXVuLu7W/Z/4IEH+NWvfmXVhoeHh8Pe\nZXH0OyyOmjeQ3NkzyZ39ktzZL0fPnRCtyWEKhfXr1+Pv70+/fv0s2371q1+xf/9+oqKi+Oyzz6wK\nAy8vL8u4tB8qKyujoaGhVWJuTU5OTg75ua4wGo0O+/laO3eqauJ89XFq607j7toZb88QFKXlnnsg\nubNfkjv7JbkTQjSHQxQKJ06c4ODBg3Ts2JE333wTgISEBPr378+aNWsoLi6mffv2jBw5so0jFeLW\nd776ONv3TUNVjSiKE1F9svDxap1VL4UQQghx63CIQqFr16788Y9/vOp7aWlprRyNEPattu40qto4\nJEFVjdTWnZZCQQghhLgNOcQ6CkII23F37YyiNN5DUBQn3F07t3FEQgghhGgLUigIIax4e4YQ1SeL\nPj3SieqThbdnSFuHJIQQQgBQUlKCRqOxPLmyoaEBX19fcnJybqi9119/nffee++6+xUVFdG1a1fi\n4+OJjIykuLgYgL///e+WdRRKSkrQ6/WWY1RV5Z577mkS27fffsuQIUMsr/38/Ni9ezcAf/3rX/nn\nP/95Q5+lJTjE0CMhhO0oigYfr1AZbiSEEKJZIlZvt1lbH6dGXXefsLAwcnNz0ev15OfnExraOr+v\nRo8eTVZWFjt27GDu3LmsXr2aSZMmodfrGTduHNOmTbMUDQArVqwgMDCwSTt33HEHp0+fBuDo0aP0\n6tWLTz/9lPDwcD799FNmzJjRKp+nOaRQEEKIG2BW4dsqLecuKvi5qdzhZUKjXP+4NmUGzbdalAoF\n1VfFfIdJ+pWFEHYnMDCQEydOALBu3TqGDx9uee/xxx+ntLQUk8nE8uXL0Wg0PP744yiKwtdff83U\nqVN55JFHGDNmDJ6enjg7OzNs2DBUVWXgwIEYjUacnZ354IMP8PDwsDrvlTWKKysrLdu0Wi2zZs3i\n0UcfJTQ0lAcffBAAs9nMmjVrSE1N5eLFi00+Q7du3Th27BiffPIJkyZN4qOPPgLg0KFD9OzZk/z8\nfF599VXq6upISUlh2rRpXLhwgdTUVLRaLZ06deKXv/wlr7zyim0v7o/IrwghhLgB31ZpyfnUneUH\n3Mn51J1vq7RtHdJ1ab7V4vqWO64r3XF9yx3Nt7d+zEIIcTUREREYDAbKy8sJCAiwbF+8eDEFBQX8\n/ve/580336RLly4UFBSwZMkSgoODSUtLY/78+fzxj3/kww8/pF27dkDjorwbNmygoKCAwYMHs2rV\nqibnXLVqFZGRkYwbN46XX37Zsj08PJwjR44wevRoy7Zly5aRmppqtdjvD/Xr149PPvmEXbt2ERcX\nR21tLWVlZXTs2BGAqKgoCgsL+eSTT1i7di2XL1/m7bffZuTIkXz00Ud06dLFJtfxeqRQEEKIG3Du\nooJJbfwFYFIVzl281bsTQKlQUEyNcSomBaXi1o9ZCCF+TFEUUlJSeP7554mNjbXc6TebzaSnpxMb\nG8vcuXMtw3tqamqYMGEC77zzDh4eHhw7dow+ffoAjV/yAWpra3nqqaeIjY3l3XffpbS0tMl5R48e\nzc6dO8nIyODjjz+2bJ81axbTp08nKyvLEsfq1asZNWoU8H89ET/Ut29fPv30U86ePYu/vz8dOnTg\nv//9r2U9sN27dzNgwADi4uIoKSnhu+++49ixYzzwwAMAlv+2NCkUhBDiBvi5qWiVxh/+WkXFz63p\nL4Jbjeqromob41S1KqrvrR+zEEJcTXBwMNHR0YwYMcKybf/+/Zw/f57CwkIyMjJQVRVVVZkwYQIz\nZ86ka9euQOOwn3379gGwZ88eAPLy8ggKCqKwsJC0tLSrfrm/4rnnniMnJwez2czRo0fZv38/06ZN\nIyoqin/961+cOXOGs2fP8tBDD7Fw4ULeeOMNy3mueOCBB9i+fTuurq4APPjggyxatIiIiAgAL6Fr\nWQAAIABJREFUFixYwFtvvUVBQQFdunRBVVWruK9Mpm5pMkdBCCFuwB1eJn7bt9ZqjsKtznyHibqJ\ntdZzFIQQ4iY1ZwJyS8jOzrZ6fffdd1NSUsKgQYO4++67AdixYweFhYWUl5cDMHnyZNLT0xkzZgwL\nFy7Ey8sLaBwKNGfOHIqLi+nUqZOlqLgaFxcXkpKSWLNmDStWrLBMYJ4+fTrx8fGkpKSwa9cuAN57\n7z1qamoICwuzaqNdu3bodDpLz0bfvn05cOCApUchJSWFRx99lHvvvdcS45NPPklqaipr166lQ4cO\n9OjR46auX3Mo6k+VTLehsrIyh1z63dXVlbq6urYOw+Z0Oh3+/v4OmzeQ3NkzyZ39ktzZL0fPnbh9\nXekh0Wg0vPTSS9x3332MHDmyRc8pPQpCCCGEEELc4urq6khKSkJVVTp16kRmZmaLn1MKBSGEuAEm\n1cxXtZWcuVxLgIs7we6+aK7xdAshhBDiZrm5uWEwGFr1nFIoCCHEDfiqtpLpn2/FqJpxUjTM7R5P\nqIdfW4clhBBC2Iw89UgIIW7Amcu1GFUzAEbVzNnLtW0ckRBCCGFbUigIIcQNCHBxx0lp/BHqpGgI\ncHFv44iEEEII25KhR0IIcQOC3X2Z2z2esz+YoyCEEEI4EulREEKIG6BRFEI9/Ij260o3Dz+ZyCyE\nEK2gpKQEjUZDUVERAA0NDfj6+pKTk3PTbdfU1JCQkEBsbCyJiYmcPHkSgHfffZeYmBgiIyN56aWX\nLPtPnz6dmJgY0tLSMJkcc10a6VEQQgghhBA3LHLlAZu1tXN0r+vuExYWRm5uLnq9nvz8fEJDQ5vV\ntqqqKD9xU0en07Fs2TICAgLYvHkzWVlZ/O1vf2Ps2LGMHz8egLi4OEpLSykvL6e0tBSDwcCcOXNY\nu3Yto0aNat6HtCPSoyCEEEIIIexGYGAgJ06cAGDdunUMHz7c8t7SpUuJiYkhKiqKwsJCoPHLfUZG\nBklJSUDj6swxMTEkJCRw7tw5y7EuLi4EBAQAjUWDVqsFwMmp8b660WjE19cXX19fdu7cycCBAwFI\nSkpix44dLfuh24gUCkIIcTPMKtrSC+gOn0FbegFksXshhGhxERERGAwGysvLLV/uKyoqWLlyJQaD\ngU2bNlktSJaUlEReXh4bNmxAq9ViMBjYsmULfn5NH2tdX1/PzJkzmTJlimXb/PnzCQ0NpWPHjrRr\n147Kykq8vLwAaN++PRUVFS38iduGFApCCHETtGeq8Hx/Dx7/+R+e7+9Be7qqrUMSQgiHpigKKSkp\nPP/888TGxqJ+f4Pm+PHjHDp0iPj4eIYOHWrVWxAeHg7A559/jl6v/8n2J06cyOTJkwkODrZsy8jI\n4Pjx45SWlrJr1y68vb2pqmr8eX/hwgV8fR3zgRZSKAghxE3QnK9DMTf+klLMKprzdW0ckRBCOL7g\n4GCio6MZMWKEZVtQUBC9e/dm69atFBQUUFxcbHlPo2n8ytu9e3fLRGjAUmRckZmZSXBwsFW79fX1\nQGOB4u7ujpubG5GRkeTn5wOQl5dH//79bf8hbwEymVkIIW6AWYWK81rau7nhrlFQzCqqRsHs7drW\noQkhRKtqzgTklpCdnW312s/Pj1GjRqHX69FqtfTq1Yvs7GyrCczJycls3LiR6OhonJ2dWb16tWX4\n0alTp5g9ezZRUVFs2bKFyMhIZs+ezdy5cyksLMRkMhEXF0fPnj0B6NixIzExMQQGBpKent56H7wV\nKeqPS6nbXFlZGQ0NDW0dhs25urpSV+d4dzp1Oh3+/v4OmzeQ3N2qyiu1/D+DO64uZuJ/cYYOag2K\nnyumzl7w/S8lyZ39ktzZL0fPnRCtSYYeCSHEDaiuVTCrCrWXtGw4dgfHfX6BqUt7S5EghBBC2Dsp\nFIQQ4gZ4uqtolMYOWY2i4ukunbNCCCEci8xREEKIG+DnbSIpupaaiwqe7ip+3o65KqcQQojblxQK\nQghxAxQF/H1N+DvmE/GEEEIImcz8Q5cuXeLSpUtNHpXlCDQaDWazua3DsDlFUXB2dqa+vt4h8waS\nO3smubNfkjv75ci58/b2buswxG1GehR+oF27dlRXVzvkkyAc+SkQ3t7e1NbWOmTeQHJnz1oyd6oZ\nLp3T0lCloPNSadfB1GrzqCV39ktyZ790Ol1bhyBuQzKZWQgh7NClc1pObHDndIE7Jza4c6lc29Yh\nCSFEiyspKUGj0VgWTWtoaMDX15ecnJybbrumpoaEhARiY2NJTEzk5MmTVu8PHjyYadOmWV5Pnz6d\nmJgY0tLSMJka56nFxcURGxtLQkICycnJHDx48KbjakvSoyCEEHaooUoB8/ddCGaFhioFV3nEuhCi\nDUQt+9ZmbW1//I7r7hMWFkZubi56vZ78/HxCQ0Ob1baqqlaLr/2YTqdj2bJlBAQEsHnzZrKysvjb\n3/4GwM6dO62OPXDgAKWlpRgMBubMmcPatWsZNWoUiqLw0Ucf4erqysGDBxk1ahTFxcW4uLg0K8Zb\njfQoCCGEHdJ5qaD5foy5Rm18LYQQt4HAwEBOnDgBwLp16xg+fLjlvaVLlxITE0NUVBSFhYVA413+\njIwMkpKSAJg8eTIxMTEkJCRw7tw5y7EuLi4EBAQAjUWDVvt/PbWLFi1i8uTJltc7d+5k4MCBACQl\nJbFjxw6gsRi5Mv/n3nvv5cEHH2T37t22vgStRnoUhBDCDrXrYKJrcq3VHAUhhLhdREREYDAYKC8v\nJyoqipqaGioqKli5ciUGg4GLFy8ydOhQYmNjgcYv8/Pnz2fDhg1otVoMBsM1266vr2fmzJksXrwY\ngG3btnHffffh7u5u2aeyspIuXboA0L59eyoqKq7aVufOnTl9+rSNPnXrk0JBCCHskKKAq79JhhsJ\nIW47iqKQkpJCamoqaWlpljv4x48f59ChQ8THx6OqqlVvQXh4OACff/45er3+J9ufOHEikydPJjg4\nGIC//vWvLFmyhL1791r28fb2pqqqCoALFy7g63v1Z2WXlpby0EMP3fiHbWMy9EgIIYQQQtiV4OBg\noqOjGTFihGVbUFAQvXv3ZuvWrRQUFFBcXGx5T6Np/MrbvXt3y0RooMljgjMzMwkODrZq9/jx46Sm\npjJt2jQ++OADPvzwQyIjI8nPzwcgLy+P/v37N2nzf//7H8XFxZYixR5Jj4IQQgghhLhhzZmA3BKy\ns7OtXvv5+TFq1Cj0ej1arZZevXqRnZ1tNQk5OTmZjRs3Eh0djbOzM6tXr8bPzw+AU6dOMXv2bKKi\noti6dSsRERHMnj3bUnAYDAb++9//MnToUAA6duxITEwMgYGBpKenA429HUOHDkWr1eLh4cHKlStx\ndnZujcvRImTBtR8pKytzyGdLO/Jzpf39/R02byC5s2eSO/slubNfjp47IVqTDD0SQgghhBBCNCGF\nghBCCCGEEKIJmaMghBDCZlQzVFdqqatRcPVQ8fQ18RPrGwkhhLiFSaEghBDCZqortezLd0c1Kyga\nlT6JtXj5yRoPQghhj2TokRBCCJupq1FQzY1dCKpZoa5GuhOEEMJeSaEghBDCZlw9VBRN48P0FI2K\nq4c8WE8IIeyVFApCCCFsxtPXRJ/EWnpE1tInsRZPXxl2JISwnZKSEjQajWXRtIaGBnx9fcnJybnp\ntmtqakhISCA2NpbExEROnjwJwJEjR4iKiiI2Npbx48db9p8+fToxMTGkpaVhMjnmzzqZoyCEEMJm\nFAW8/Ex4+bV1JEKI1vLrd2tt1tb7492vu09YWBi5ubno9Xry8/MJDQ1tVtuqqlotvvZjOp2OZcuW\nERAQwKZNm1iwYAGLFi0iJyeHmTNnkpiYyFNPPcUnn3yCm5sbpaWlGAwG5syZw9q1axk1alSzP6e9\nkB4FIYQQQghhNwIDAzlx4gQA69atY/jw4Zb3li5dSkxMDFFRURQWFgIQFxdHRkYGSUlJAEyePJmY\nmBgSEhI4d+6c5VgXFxcCAgIAcHZ2RqNp/Jp8zz33UFFRAUBVVRW+vr7s3LmTgQMHApCUlMSOHTta\n9kO3ESkUhBBCCCGEXYmIiMBgMFBeXm75cl9RUcHKlSsxGAxs2rSJzMxMy/5JSUnk5eWxYcMGtFot\nBoOBLVu24OfXtPuzvr6emTNnMmXKFAAGDBjAjBkz6NGjB87OzoSGhlJZWYmXlxcA7du3txQSjkYK\nBSGEXTOpJr6sPcq2iiK+rD2KWTW3dUhCCCFakKIopKSk8PzzzxMbG4uqNj404fjx4xw6dIj4+HiG\nDh1q1VsQHh4OwOeff45er//J9idOnMjkyZMJDg4GYMaMGbz77rscPnwYHx8fNm7ciI+PD1VVVQBc\nuHABX1/flviobU4KBSGEXfvq4nFmHJ3GX75ZwIyj0zh+8VhbhySEEKKFBQcHEx0dzYgRIyzbgoKC\n6N27N1u3bqWgoIDi4mLLe1eGEXXv3t0yERqwFBlXZGZmEhwcbNUuYOl56NChA1VVVURERJCfnw9A\nXl4e/fv3t+0HvEXIZGYhhF07c/k0RtUIgFE1cubyabq5N29imxBCiJvXnAnILSE7O9vqtZ+fH6NG\njUKv16PVaunVqxfZ2dlWE5iTk5PZuHEj0dHRODs7s3r1aksRcOrUKWbPnk1UVBRbtmwhMjKS2bNn\nk5GRwdNPP41Op8PHx4cXX3wRZ2dnOnbsSExMDIGBgaSnp7fqZ28tivrjUuo2V1ZWRkNDQ1uHYXOu\nrq7U1dW1dRg2p9Pp8Pf3d9i8geTuer6sPcqMo9MwqkacFCfmhGbdMoWC5M5+Se7sl6PnTojWJD0K\nQgi7FuwWwpzQLM5cPk2AS2eC3ULaOiQLs9lI7fmjXL54Bhe3ANzah6AoMuJTCCGEfZBCQQhh1zSK\nhm7uobdML8IPnS/7nKMfZ6CqRhTFidCI+bh733pxCiGEEFcjt7aEEKKFXKo9jfr9/AlVNXL54pk2\njkgIIYRoPofoUVi/fj1Hjx7F3d2d3/72twAUFhayd+9e3N0bJ9gkJCTQrVu3tgxTCHGbaefeGUVx\nsvQouLgFtHVIQgghRLM5RKFw33338eCDD7Ju3Tqr7REREURGRrZRVEIIh2Y2o/3uDJrzlZi9fTB1\n6gw/eLIGgLd/D0Ij5lvNUWhpqhlM32kxn1fQeKtoO5l+HJYQQgjRLA5RKAQGBnL+/Pm2DkMIcRvR\nfncGr1VLUcxmVI2GqlFpmAK6WO2j0Whx9w5t1XkJpu+01K50B7MCGhX3UbU4dTa12vmFEEI4Doee\no7Br1y7eeOMN1q9fz6VLl9o6HCGEA9Gcr0QxN64CrZjNaM5XNvtYk6ryZXUN28rL+bK6BrMNn1Jt\nPq80FgkAZgXzBelOEEI4jpKSEjQajWXRtIaGBnx9fcnJybnptmtqakhISCA2NpbExEROnjwJwJEj\nR4iKiiI2Npbx48db9p8+fToxMTGkpaVhMjXekKmqquKJJ55Ar9cTHR3Nn//855uOqy05RI/C1YSH\nh6PX61EUhS1btpCXl8cjjzxieb+qqoqamhqrYzw8PHBycsxLotVq0el0bR2GzV3Jl6PmDSR3tyrV\nxxdVo7H0KKg+vk3ydK3cHTt/nhmHDmNUVZwUhbn33kN3b2+bxGXyMYNGtfQoaH1Um//9sffcNYf8\nu7Nfjp67W9HcnFqbtfXib6+/eFtYWBi5ubno9Xry8/MJDW1er62qqlaLr/2YTqdj2bJlBAQEsGnT\nJhYsWMCiRYvIyclh5syZJCYm8tRTT/HJJ5/g5uZGaWkpBoOBOXPmsHbtWkaNGsWUKVNISkriscce\nAxrnzNqzW/dv3U26MokZ4IEHHmD58uVW7+/du9dqCW8AvV5PXFxcq8QnbMvHx6etQxA3yF5z1+Dt\nTc3Yp6CiAnz98PlV92Z/OTF8V4bx+14Eo6pSVt9AjI0WUmrwbuDcuGqMFeDkC36/alrA2Iq95k5I\n7oR9CwwM5MSJEwCsW7eO4cOHW95bunQpixcvxmw28+qrrxIbG0tcXBwPPvgg+/fvJy8vj8mTJ3Pg\nwAF0Op3VyswuLi4EBDQ+dMLZ2RmNpnHgzT333ENFRQXQeKPZ19eXrVu3MnDgQACSkpJYsmQJI0eO\n5OOPP2bp0qWWeGJjY1v8erQkhykUfrzAdHV1NZ6engB8/vnndOzY0er9Bx54gF/96ldW2zw8PKis\nrMRoNLZssG3AxcWFy5cvt3UYNufk5ISPj4/D5g0kd7e0Tl0a/8BV50ldK3f+zjqcFMXSo+DvrKOs\nrMxmYWk7Nf65Vlw3yyFydx3y785+OXruRKOIiAgMBgPl5eVERUVRU1NDRUUFK1euxGAwcPHiRYYO\nHWr5op6UlMT8+fPZsGEDWq0Wg8Fwzbbr6+uZOXMmixcvBmDAgAEMGDCAmTNn0qdPH0JDQ/nggw/o\n0qXx53/79u2pqKigvLzc4VbPdohCYe3atXzzzTfU1dXxl7/8hbi4OL7++mvOnDmDoih4e3uTnJxs\ndYyXlxdeXl5N2nLUZe2dnJwc8nNdYTQaHfbzSe7s17VyF+Tmxpx7enDm8iUCXNoR5OZml9fgdsyd\no7iVcqeqJoyVxzHWnMHJIwAnn5tbwdzRcydAURRSUlJITU0lLS3NcrP4+PHjHDp0iPj4eFRV5dy5\nc5ZjwsPDgcabx3q9/ifbnzhxIpMnTyY4OBiAGTNm8O677xIdHc2UKVPYuHEjPj4+VFVVAXDhwgV8\nfX3p0KGDTW/63AocolAYMWJEk233339/G0QihBDXp1EUunl60M3To61DEaLNGSuPU7E1A1QjKE74\nxs9H5ysrmIufFhwcTHR0NCNGjGDz5s0ABAUF0bt3bzZs2ABgmWAMWIYRde/enfz8fMtwpR/PW8jM\nzCQ4OLjJd8srw5M6dOhAVVUVERERvPbaa/z6178mLy+P/v37o9FoiIyMZPny5YwZMwaAoqKi6xYm\ntzKHKBSEEEIIYZ+MNWcaiwQA1Yix5owUCnamOROQW0J2drbVaz8/P0aNGoVer0er1dKrVy+ys7Ot\nCoHk5GQ2btxIdHQ0zs7OVnMUTp06xezZs4mKimLLli1ERkYye/ZsMjIyePrpp9HpdPj4+PDiiy/i\n7OxMx44diYmJITAwkPT0dAAWLVrElClTeOuttzCbzTz66KN2XSgo6o8H99/mHHXokaurK3V1dW0d\nhs3pdDr8/f0dNm8gubNnkjv7JblrPQ0VR23ao+DouROiNUmPghBCCCHajJNPCL7x863mKAghbg1S\nKAghRCtTVRPVVcepu3gaV7fOeHrd3ORNIeyZomjQ+YbKcCMhbkFSKAghRCurrjpO8e5pqKoRRXHi\n/vAsvNrLlyQhhBC3FrmFJYQQrazu4mnU7ydvqqqRuoun2zgiIYQQoikpFIQQopW5unVGURo7dBXF\nCVe3zm0ckRBCCNGUDD0SQohW5ukVwv3hWVZzFIQQQohbjfQoCCFEK1MUDV7tQ+nUWY9X+1CZyCyE\nEM1UUlKCRqOhqKgIgIaGBnx9fcnJybHpOdq1a8fhw4eBxkXYevXqRXx8vGW9BIDp06cTExNDWlqa\nZXG37OxsIiIi0Ov1TJ48ucnx8fHxbNu2zWaxtjTpURBCCCGEEDds2cJam7X1+AvXX7wtLCyM3Nxc\n9Ho9+fn5hIY272EQP16F+VoWLFhAVFSU1bZ58+YxZMgQy+sDBw5QWlqKwWBgzpw5rF27lqFDh7Jq\n1So+/vhjAC5cuHDN4+2F3MYSQgghhBB2IzAwkBMnTgCwbt06hg8fbnlv6dKlxMTEEBUVRWFhIQBx\ncXFkZGSQlJQEwOTJk4mJiSEhIYFz585Ztf3NN9+gKApdu3a12v6HP/yBuLg4CgoKANi5cycDBw4E\nICkpiR07dqDRaDh37hx79+4FoH379pbj7XV9YykUhBBCCCGEXYmIiMBgMFBeXk5AQAAAFRUVrFy5\nEoPBwKZNm8jMzLTsn5SURF5eHhs2bECr1WIwGNiyZQt+fn5W7c6fP5/09HSrL/bPPvss+/btY9Wq\nVTz77LMYjUYqKyvx8vICGguCiooK3NzcyMnJ4eWXXyYkJIS3337b0saLL75IXFwc8fHxfPnlly15\naWxKhh4JIYQQQgi7oSgKKSkppKamkpaWZvlSf/z4cQ4dOkR8fDyqqlr1FoSHhwPw+eefo9frr9ru\nV199ZdWbcKVdb29vADp27Ej37t05deoU3t7eVFVVAY1DjHx9fQFITEwkMTGR2tpaIiMjGTNmDCBD\nj4QQQgghhGgVwcHBREdHM2LECMu2oKAgevfuzdatWykoKKC4uNjynkbT+JW3e/fulonQYD0k6LPP\nPuPQoUMMHjyYzZs3M2nSJOrr66murgbg4sWLHDlyhM6dOxMZGUl+fj4AeXl59O/fn/r6ek6dOgWA\nm5sbrq6uVz2PPZEeBSGEEEIIccOaMwG5JWRnZ1u99vPzY9SoUej1erRaLb169SI7O9tqAnNycjIb\nN24kOjoaZ2dnVq9ebRl+NGzYMIYNGwbAhAkTSE9Px9nZmWeeeYaDBw9iNpt56aWXcHFxoXfv3nTs\n2JGYmBgCAwNJT0/n0qVLjB8/nsuXL2MymRg3bhxubm4AzJgxg4ULFwLw+9//noceeqg1LtFNU1R7\nLXFaSFlZGQ0NDW0dhs25urpSV1fX1mHYnE6nw9/f32HzBpI7eya5s1+SO/vl6LkTojVJj4IQQtwG\nVLMJteI4avUZFM8AFL8QWb9BCCHET5JCQQghbgNqxXGMGzPAbASNE05J81E6NO/Z40IIIW5PcjtJ\nCCFuA2r1mcYiAcBsbHwthBBC/AQpFIQQ4jageAaA5vtOZI1T42shhBDiJ8jQIyGEuA0ofiE4Jc23\nmqMghBBC/BQpFIQQ4jagKJrGOQkyL0EIIUQzydAjIYQQQghhF0pKStBoNJZF0xoaGvD19SUnJ8em\n52jXrh2HDx8GIDMzk169ehEfH096erplv+nTpxMTE0NaWhomkwloXNshIiICvV7P5MmTLcd/9NFH\nNouvNUmPghBCCCGEuGEFf6q1WVtxr1x/8bawsDByc3PR6/Xk5+cTGtq8nlJVVa0WX7uWBQsWEBUV\nZbVt3rx5DBkyxPL6wIEDlJaWYjAYmDNnDmvXrmXo0KGsWrWKjz/+GIALFy40K65bmfQoCCGEEEII\nuxEYGMiJEycAWLduHcOHD7e8t3TpUmJiYoiKiqKwsBCAuLg4MjIySEpKAmDy5MnExMSQkJDAuXPn\nrNr+5ptvUBSFrl27Wm3/wx/+QFxcHAUFBQDs3LmTgQMHApCUlMSOHTvQaDScO3eOvXv3AtC+fXvb\nf/hWJoWCEEIIIYSwKxERERgMBsrLywkIaHyKW0VFBStXrsRgMLBp0yYyMzMt+yclJZGXl8eGDRvQ\narUYDAa2bNmCn5+fVbvz588nPT0dVVUt25599ln27dvHqlWrePbZZzEajVRWVuLl5QU0FgQVFRW4\nubmRk5PDyy+/TEhICG+//XYrXImWJUOPhBBCCCGE3VAUhZSUFFJTU0lLS7N8qT9+/DiHDh0iPj4e\nVVWtegvCw8MB+Pzzz9Hr9Vdt96uvvrLqTbjSrre3NwAdO3ake/funDp1Cm9vb6qqqoDGIUa+vr4A\nJCYmkpiYSG1tLZGRkYwZM6YFrkDrkR4FIYQQQghhV4KDg4mOjmbEiBGWbUFBQfTu3ZutW7dSUFBA\ncXGx5T2NpvErb/fu3S0ToQGrnoPPPvuMQ4cOMXjwYDZv3sykSZOor6+nuroagIsXL3LkyBE6d+5M\nZGQk+fn5AOTl5dG/f3/q6+s5deoUAG5ubri6ul71PPZEehR+4NKlS+h0OpycHO+yaDQaq7+wjkJR\nFC5evOiweQPJnT2T3NkvyZ39cuTc3aqaMwG5JWRnZ1u99vPzY9SoUej1erRaLb169SI7O9vq2iUn\nJ7Nx40aio6NxdnZm9erVluFHw4YNY9iwYQBMmDCB9PR0nJ2deeaZZzh48CBms5mXXnoJFxcXevfu\nTceOHYmJiSEwMJD09HQuXbrE+PHjuXz5MiaTiXHjxuHm5gbAyy+/zF//+legcbJ07969W+MS3TRF\ntdcSp4WUlZXR0NDQ1mHYnKurK3V1dW0dhs3pdDr8/f0dNm8gubNnkjv7JbmzX46eOyFakww9EkII\nIYQQQjQhhYIQQgghhBCiCSkUhBBCCCGEEE1IoSCEEEIIIYRoQgoFIYQQQgghRBNSKAghhBBCCCGa\n+P/t3Xl8k1Xa//FPWloKtGVHNoetZRNZbAsFm7YJBYs8ilA2RWRTh0VA0DIozvOSB8UO8ELFUQaU\nRURgoBZwWFXohiAwHWF8YKAtQrFsspSttJQm+f3B0/wIQSm1EBK/77/InXOf+8p9gebKOec+KhRE\nRERExC3k5OTg5eVl3zTt2rVr1KhRg48++qhcr+Hn58f+/fsBmDp1Km3btsVsNhMfH29vN3nyZCIj\nIxkyZAgWiwW4vrdD586diYqK4qWXXrKfv2HDBodrHD9+nLi4OKKjozEajSxdurTc4i9PnrnbioiI\niIjcE/95Nb/c+mo16/abt4WGhpKUlERUVBTffPMNzZs3L1XfNputVBvXzZw5k4iICIdjCQkJPP74\n4/bX//73vzl+/DhpaWlMnz6dxMREevbsyd///nd27NgBwIULF37xGs8++yzTpk3j0UcfBSA9Pb1U\nn+Fe04iCiIiIiLiNRo0acfToUQBWr15Nnz597O99+umnREZGEhERQUpKCgAmk4k//elPxMbGAvDS\nSy8RGRlJ165dOXv2rEPfR44cwWAw8Ic//MHh+BtvvIHJZCI5ORmA7du30717dwBiY2P59ttv8fLy\n4uzZs2RkZABQtWrVW8afm5uLzWazFwkARqOxrLfjrlKhICIiIiJupXPnzqSlpXHmzBnq1q0LwLlz\n51ixYgVpaWl89dVXTJ061d4+NjaWzZs3849//ANvb2/S0tLYsmULNWvWdOj3L3/5C/HgQJWgAAAg\nAElEQVTx8dhsNvux8ePH869//Yu///3vjB8/nuLiYvLy8ggMDASuFwTnzp2jcuXKfPTRR/z5z38m\nKCiITz755JaxHz9+nPr165f3LbkrNPVIRERERNyGwWAgLi6O/v37M2TIEPuX+kOHDrFv3z7MZjM2\nm81htCAsLAyA//znP0RFRd2y3x9//NFhNKGk32rVqgFQp04dWrVqRW5uLtWqVePixYvA9SlGNWrU\nACAmJoaYmBjy8/Pp0qULzzzzjNN16tevT25ubnncirtOIwoiIiIi4laaNWuG0Wikb9++9mNNmzal\nXbt2bN26leTkZL7//nv7e15e17/ytmrVyr4QGnAYOdi7dy/79u2jR48efP3114waNYqioiIuXboE\nwJUrVzh48CD16tWjS5cufPPNNwBs3ryZRx99lKKiInsBULlyZSpVqnTL6zRs2JAKFSrw7bff2o/d\nr2sUNKIgIiIiImVWmgXId8N7773n8LpmzZoMGDCAqKgovL29adu2Le+9957DAuYnnniCTZs2YTQa\n8fX1ZeXKlfbpR71796Z3794ADB8+nPj4eHx9fRk3bhw//PADVquVKVOmULFiRdq1a0edOnWIjIyk\nUaNGxMfHU1hYyLBhw7h69SoWi4XnnnuOypUrA/DnP/+Z999/H7i+WHrp0qWMGTOGKVOmYLFYGDly\n5L24ZXfMYLuxxBFOnz7NtWvXXB1GuatUqRIFBQWuDqPc+fj4ULt2bY/NGyh37ky5c1/Knfvy9NyJ\n3EuaeiQiIiIiIk5UKIiIiIiIiBMVCiIiIiIi4kSFgoiIiIiIOFGhICIiIiIiTlQoiIiIiIiIExUK\nIiIiIuIWcnJyqFOnDmazmfDwcDIyMu7o/LVr13LmzBng+kZpa9euLVMcU6dOpW3btphMJmJjYwGY\nOHEiV69eLdX5w4YNY//+/Q7H9u7dy7x58371vPXr1zN16tQyxVwW2nBNRERERMrs4pD8cusr8NPb\nb94WHR3NypUr2bVrF6+//jqbN28udf9r1qwhKCiIWrVq8dhjj/2WUElISODxxx+3v549e7ZTG5vN\n5rDh269p164d7dq1u+35pe2vPKhQEBERERG30759e3JzcwF45ZVX2LVrFxUrVmThwoX84Q9/oHXr\n1nTq1Im9e/cSHx9P586d2bRpE/v378dkMtGqVSvy8/MZPXo0gwYN4vjx41gsFpYtW0bDhg3ZsGED\n06ZNo1KlSowYMYJBgwY5XP/mPYtNJhPr169n1apVbNq0iStXrjBq1CgMBgNvv/02VquVsWPHMmDA\nAOB6YZGTk0OtWrVYtmwZaWlprFu3jpkzZxISEoLRaOTs2bN8+OGHDBgwAIPBQNWqVWnVqtW9ucFo\n6pGIiIjcDVYL3qcy8TmYhvepTLBZy9yVxWYj6/J50s8cI+vyeaw3fUGT35eSL+gpKSm0bNmSjIwM\nTpw4QXp6Om+++aZ9as6pU6f461//SmpqKu+//z6NGzcmNjaWRYsWkZCQ4NDnggULSE5OZuLEicyb\nNw+bzcbrr7/ON998w9atW52KBIDXXnsNs9nM5MmTAcdf+n19fVm7di2xsbFMmzaNrVu3kpaWxgcf\nfGCPPzw8nK+//pomTZrYp0CV9JGXl8f48eP57LPP+Pjjj4mLi2PDhg00bty4fG/mbWhEQURERMqd\n9+lDBK76EwZrMTavClzs+xcsdZuXqa8f8y/w2v7tFNtsVDAYmN66C839q5VzxOIuUlNTMZvN+Pv7\n895777F7927CwsIACAsLY8qUKQA0bdqUKlWuT2WyWn+5ULVarcTHx/PDDz9QUFBAmzZtOH36NA8+\n+KD9/Fu5eerRjUriOX36NJmZmXTv3h2bzcbFixc5ffo0ACEhIQCEhoaSlZVFx44d7efXqFGDJk2a\nAJCdnc2LL75o7/d///d/b3+TyolHFApr164lMzOTKlWqMHr0aAAKCgpYtWoVFy5coFq1avTr1w8/\nPz8XRyoiIvL74HX+JAZrMQAGazFeF06WuVA4WZhP8f/9Cltss3GqMF+Fwu9YyRqFEnl5efZf5Hft\n2kVwcPAvnuvj44PFYnE4tmfPHs6fP09KSgpJSUmsW7eO2rVrc+zYMfLz86lSpUqp1hrcOBXJy+v6\npJ1atWrRqlUrvvrqKypUqIDFYsHb2xuA77//ng4dOvDPf/7TXliUuPFawcHB/Otf/7K3vZffZz2i\nUGjfvj0dO3Zk9erV9mPbtm2jadOmREREsG3bNtLT0+nWrZsLoxQREfn9sFari82rgn1EwVqtbpn7\nqutXhQoGg31Eoa7f7Re8yr1TmgXId1NISAh169bFaDTi4+PDokWLgFsv+u3Rowcvv/wyMTExNGjQ\nAICWLVuSk5PDY489RsuWLe3nvvXWW3Tt2pUqVaowfPhwh+lHt+r7l45NmTKFmJgYvLy8qFOnDitW\nrAAgIyODZcuWUatWLd566y3S09Nv2deIESPo378/q1atol69evaRhnvBYLt5JYabOn/+PMuWLbOP\nKHzwwQcMGzYMf39/Ll26xOLFixk7duxt+zl9+jTXrl272+Hec5UqVaKgoMDVYZQ7Hx8fateu7bF5\nA+XOnSl37ku5Kwc2K96nsvG6cBJrtbpY6gSBoWxLI602G9n5FzhVmE9dvyo0q1IVr1/4ddfTcydy\nL3nEiMKt5Ofn4+/vD0BAQAD5+eX36C4RERG5DYMXlrrNyzzd6EZeBgPN/atpupHIPeaxhcLNbh4O\nunjxIpcvX3Y45u/vT4UKnnlLvL298fHxcXUY5a4kX56aN1Du3Jly576UO/fl6bkTuZc89m+dv78/\nly9ftk89unnVekZGBqmpqQ7HoqKiMJlM9zJMKSfVq1d3dQhSRsqd+1Lu3JdyJyKl4TGFws1LLVq0\naMGePXuIiIhg7969tGjRwuH9kJAQp2P+/v7k5eVRXFx81+O91ypWrFjqbcXdSYUKFahevbrH5g2U\nO3em3Lkv5c59eXruRO4ljygUEhMTOXLkCAUFBcyePRuTyURERAQrV67k+++/p2rVqvTr18/hnMDA\nQAIDA5368tTFeRUqVPDIz1WiuLjYYz+fcue+lDv3pdy5L0/Pnci95BGFQt++fW95fMiQIfc4EhER\nERERz1C255SJiIiIiNxjOTk51KlTB7PZTHh4OBkZGeXW99SpU9mwYcMdnWO1Whk8eDBdu3bl+eef\nt+8A/dprr9GgQQMmTZrkdM7IkSPp37+/0/EjR44QGRmJyWTiiSee4NKlS2X7IOXII0YURERERMQ1\nrgz/sdz6qryw6W3blOzMvGvXLl5//XU2b978q+1Ls6tyWa1evZqmTZvy2WefMWvWLJKSkujbty8T\nJkwgNjaW9evXO7TPycnhxIkTVKxY0amv6tWrs27dOgIDA/n444/5+OOPmThx4l2Ju7Q0oiAiIiIi\nbqd9+/bk5uYC8Morr2A0GomJieHo0aMAPPTQQ4wYMYJXXnmFefPm0alTJ2JiYli7di0A06dPp0uX\nLpjNZvbt2wfAihUr6NmzJyaTyb4o/p133iE6Opro6Gh7uxKHDh2iffv2AHTo0IG0tDQA6tSpc8uY\nZ8yYwauvvnrL96pWrWpfP+vj44OXl+u/pmtEQURERETcRsmTLlNSUmjZsiUZGRmcOHGC9PR0tm3b\nxtSpU1mwYAHHjh3j3XffJTAwkJiYGLZs2WLfjPff//43u3fvZvv27Q59Nm/enDfeeIPXXnuNr7/+\nmiZNmnDw4EFSUlI4ceIEo0aNYs2aNfZYWrduzebNm+nduzfffPMNeXl5vxj34cOHMRgMNGrU6Fc/\n3/nz5/nb3/7Gpk2bftN9Kg+uL1VEREREREopNTUVs9nMX//6V2bOnEl2djZhYWEAhIWFkZ2dDUBQ\nUJD9F/qEhATGjRvH8OHDyczM5MCBAxiNRnufJVOTOnToAEDDhg3Jy8tj//79bN++HbPZzKBBg7hy\n5YpDLP/1X/+Fn58fMTExXLlyhbp16/5i3AkJCcTHx2O1Wu2FyfLlyzGZTMTHxwPXn0j27LPP8u67\n71Ktmut3IteIgoiIiIi4jZI1CiXy8vLs04l27dpFcHAwgMO6hDZt2rBw4UJ27NjBjBkzePnll1m+\nfLl9DUDJF/cbz7HZbLRs2ZLo6Gjmz58PgMVicYpn5syZwPXF0F27dnV478Z9vnJychg1ahRXrlwh\nMzOThQsXMnz4cJ5++ml7mxdeeIEBAwbQuXPnMtyZ8qdCQURERETKrDQLkO+mkJAQ6tati9FoxMfH\nh0WLFgGOX/pHjRrFkSNHKCoqYvr06bRp04bQ0FA6d+5M5cqVmTNnzi0XPD/88MMEBQURHR2Nt7c3\n3bp1Y/Lkyfb3T506xdNPP423tzddu3YlIiICgDlz5rBkyRLOnj3L8ePH+fzzz+1TiXJycoiPj2f4\n8OEO19q2bRuJiYnk5OSwaNEievfuzdixY8v9ft0Jg+3mLY1/5zx1w7VKlSpRUFDg6jDKnY+PD7Vr\n1/bYvIFy586UO/el3LkvT8+dyL2kNQoiIiIiIuJEhYKIiIiIiDhRoSAiIiIiIk5UKIiIiIiIiBMV\nCiIiIiIi4kSFgoiIiIiIOFGhICIiIiJuobCwEJPJhMlkIjAwELPZjNls5vz582Xqz2azsWDBglK3\nP3bsGL179yYqKgqj0cjy5cvLdF2ABQsWMH/+fCwWy32zwdrNtOGaiIiIiJTZlT/uLLe+Ks/r9Kvv\n+/n5kZycDEDHjh3ZunXrb7qe1Wrlk08+YcSIEaVq/8wzzzBjxgw6dboeZ3p6+m3Psdlst9zM7Ua3\ne99VNKIgIiIiIm6nZM9gi8XCoEGDiI6OplevXly4cIFZs2axatUqAA4cOMCwYcMAmDBhApGRkXTr\n1o2ffvqJuXPnsn//fsxmM+np6SQkJGAymQgLC7MXJCWOHDmCr6+vvUgAMBqNTv3m5uZisVho27Yt\nw4cPZ9KkSfz000/ExMQQFRXFhAkTfvEzLV68GJPJRGhoKCtWrCjX+1UWKhRERERExO2U/AqfmJhI\ns2bNSElJoXfv3sydO5fBgwfbv2gvXbqUwYMHs3PnTs6ePUtaWhpTpkzhrbfeYvTo0Tz00ENs3boV\no9HIyy+/THJyMuvWrWPatGkO1zt+/Dj169d3iuPmfkvOy83NZc6cOcycOZPp06czZcoUUlNTOX/+\nPDt27HDoo6ToGThwIMnJyaSnpzNr1qxyv2d3SoWCiIiIiLit7OxswsLCAAgLCyMrK4sHHngAq9XK\nmTNnSE1NxWw2O7XLzs7GZrPZv6QDLFy4kMjISAYOHMipU6ccrlO/fn1yc3Nve/3s7GwAmjdvjr+/\nv71NaGgoAKGhofY2JUqKnnXr1mEymejRoweHDx/+zffmt1KhICIiIiJup+QLflBQEDt3Xl8nsXv3\nboKDg4Hrv86PGzeOLl262Nvt3r0bgF27dhEcHIy3t7dDoTB37lzS0tJYvnw5FovF4XqNGzfGYrHw\n3Xff2a+fnp5OUFAQu3btcugXHNcdBAcH3zLGm73zzjts2rSJDRs2ULFixd9wd8qHFjOLiIiISJnd\nbgHy3VLyRTwuLo41a9YQHR1NQEAAS5cuBeCpp55i5MiRpKWlAdCpUyc+//xzjEYjvr6+LF68GICm\nTZvSr18/4uPj6dy5MxEREYSHh9tHA260bNkyxowZw7lz57BarYwZM4aBAwfest8bC4XJkyczdOhQ\npk2bRvv27QkPD2ffvn1O/ffq1YuIiAg6dOhAjRo1yvN2lYnBdmMZJZw+fZpr1665OoxyV6lSJQoK\nClwdRrnz8fGhdu3aHps3UO7cmXLnvpQ79+XpuZPSKywspGfPnmzZssXVobgtTT0SEREREY9y8OBB\nunfvzvjx410dilvT1CMRERER8SgtWrSwTzmSstOIgoiIiIiIOFGhICIiIiIiTlQoiIiIiIiIExUK\nIiIiIiLiRI9HvUFhYSGFhYV44i3x8vLCarW6OoxyZzAY8PX1paioyCPzBsqdO1Pu3Jdy5748OXfV\nqlVzdRguV1hYSI8ePQDIyMiw73aclJRUpvtjs9lYuHAhI0aMKFX7Y8eO8dJLL9n3URg9ejRPP/30\nHV/XXahQuImnPlva058r7al5A+XOnSl37ku5c1+enrv7UcGY9eXWV6UPe5a6bceOHe07IpeVxWIh\nIiKCHTt2lKp9VFQUM2bMoFOn65vMpaenYzQaf/Ucm83msPmaO9HUIxERERFxOyW/dVssFgYNGkR0\ndDS9evXiwoULzJo1i1WrVgFw4MABhg0bBsCECROIjIykW7du/PTTT8ydO5f9+/djNptJT08nISEB\nk8lEWFgYycnJDtc7cuQIvr6+9iIBsBcJN/abm5uLxWKhbdu2DB8+nEmTJvHTTz8RExNDVFQUEyZM\nAGDBggX069ePJ598kvDwcE6fPg3AwIEDMZlMREVFcfz48bt7E29DhYKIiIiIuJ2SX+kTExNp1qwZ\nKSkp9O7dm7lz5zJ48GBWrFgBwNKlSxk8eDA7d+7k7NmzpKWlMWXKFN566y1Gjx7NQw89xNatWzEa\njbz88sskJyezbt06pk2b5nC948ePU79+fac4bu635Lzc3FzmzJnDzJkzmT59OlOmTCE1NZXz58/z\n3XffAVCzZk2+/PJLnn32Wb744gsAFi9eTHJyMmPHjmX+/Pl37f6VhgoFEREREXFb2dnZhIWFARAW\nFkZWVhYPPPAAVquVM2fOkJqaitlsdmqXnZ2NzWZzWK+zcOFCIiMjGThwIKdOnXK4Tv369cnNzb3t\n9bOzswFo3rw5/v7+9jYl6ylCQ0PJysoCoEOHDgA8+OCD5OXlYbFYmDhxItHR0cyYMYMTJ06U230q\nCxUKIiIiIuJ2Sr7gBwUFsXPnTgB2795NcHAwcH0Kz7hx4+jSpYu93e7duwHYtWsXwcHBeHt7OxQK\nc+fOJS0tjeXLl2OxWByu17hxYywWi300wGazkZ6eTlBQkH2tREm/gMO6hODg4FvGeGMbm81GRkYG\nBQUFpKSk8Oqrr7r8oQMVXHp1EREREXFrd7IAuTyVfMmOi4tjzZo1REdHExAQwNKlSwF46qmnGDly\nJGlpaQB06tSJzz//HKPRiK+vL4sXLwagadOm9OvXj/j4eDp37kxERATh4eH20YAbLVu2jDFjxtif\nejRmzBgGDhx4y35vLAImT57M0KFDmTZtGu3btyc8PJx9+/Y59d+6dWuys7OJjY21FxOupKce3cRT\nnwTh6U+B8NS8gXLnzpQ796XcuS9Pz52UXmFhIT179mTLli2uDsVtaeqRiIiIiHiUgwcP0r17d8aP\nH+/qUNyaph6JiIiIiEdp0aKFfcqRlJ1GFERERERExIkKBRERERERcaJCQUREREREnKhQEBERERER\nJyoURERERMQtREZG8vPPP9tff/bZZ7z99ttO7T799FM++ugjAEaNGnVH17hw4QKrVq36bYF6CD31\nSERERETKrGDcknLrq9Kc5371/b59+/LFF1/Yv/wnJiYya9asXz1n7ty5dxTD+fPnWblyJf369buj\n8zyRRhRERERExC3ExcWRlJQEwKVLlzh58iRHjx4lOjqaTp06MWPGDKdzwsLCAFi6dCkmk4nQ0FA+\n//xzAKZOncpzzz1Hz549MZlMXL16lblz55KamorZbObAgQP37sPdh1QoiIiIiIhbaNCgAUVFRZw9\ne5Z//OMfPPnkkzz66KOkpKTw3XffkZiYyNWrVx3OMRgMwPXRiOTkZLZt28bs2bPt7zdv3pz169cT\nHh7O119/zahRo4iOjmbr1q20bNnynn6++42mHomIiIiI2+jTpw9JSUls3LiRhIQE/vnPfzJ16lSu\nXbtGTk6OwxqGG23cuJE5c+Zgs9k4dOiQ/XiHDh0AaNiwIXl5effkM7gLFQoiIiIi4jbi4uIYOHAg\nxcXFNG/enFdffZV58+bRpEkTHnnkEWw2m0P7ktdvv/026enpADRr1sz+fsmIQ0lbHx8fiouL78En\nuf+pUBARERGRMrvdAuTy1rBhQ2w2G0888QRwvXB46qmnePjhhwkMDHRqX1II9OnTB6PRSIcOHahR\no8Yv9l+vXj0KCgro378/77zzjkNR8XtjsN1cdv3OnT59mmvXrrk6jHJXqVIlCgoKXB1GufPx8aF2\n7doemzdQ7tyZcue+lDv35em5E7mXtJhZREREREScqFAQEREREREnKhRERERERMSJCgUREREREXGi\nQkFERERERJx4/ONR3333Xfz8/DAYDHh5efHiiy+6OiQRERERkfuex48oGAwGhg4dysiRI1UkiIiI\niLixyMhIh52XP/vsM95+++1btv3000/56KOPynSdixcv0qlTJwIDA9m/f7/De/n5+dSpU4cNGzYA\n8MknnxAeHs6jjz7KrFmz7O0CAwMxm82YTCZGjBhRpjhczeNHFACnHfpEREREpHwUTvxLufXlN/tP\nv/p+3759+eKLLxg1ahQAiYmJDl/Oy0uVKlXYsGED8fHxTu/NmTOH0NBQ++tu3brx/PPPA2A0Ghk6\ndCi1atWiZcuWbN26tdxju5c8fkQBYMmSJcyfP5+MjAxXhyIiIiIiZRQXF0dSUhIAly5d4uTJkwQH\nB3Ps2DG6detGdHQ048aNczjniy++YMaMGcD10YCuXbsCMHv2bLp06UJkZCR79uxxOMfb25uaNWs6\n/dh86dIlfvjhB8LDw+3HGjVqZP+zj48PXl7Xv157wg/VHj+iMGLECAICAsjPz2fJkiXUqlWLRo0a\ncfHiRS5fvuzQ1t/fnwoVPPOWeHt74+Pj4+owyl1Jvjw1b6DcuTPlzn0pd+7L03P3e9egQQOKioo4\ne/Ysmzdv5sknnwQgISGBSZMm0a1bN1544QXS09Pt5/Ts2ZPY2FgmTZrEl19+Sa9evTh16hRffvkl\n27dvJycnhxdeeIGvvvrqttd///33GTt27C3brl69mqCgIGrUqAHAwYMHMZvNAHTu3PkXp0jdzzz+\nb11AQABwfQipVatWHDt2jEaNGpGRkUFqaqpD20aNGhEXF0f16tVdEaqUwcWLF0lOTiYkJER5czPK\nnftS7tyXcue+bsxdYGCgq8NxqT59+pCUlMTGjRtJSEgAIDs72z4dKDQ0lOzsbPsv+35+fjRq1Iis\nrCwSExP58MMPOXLkCO3atQOuf/+7cOHCba978eJF9u7dyxtvvMFXX33lMGKwd+9ePvzwQ9avX28/\n5glTjzy6UCgqKsJms1GxYkWKioo4dOgQUVFRAISEhNCiRQt729OnT7N69WouX778u/8H6E4uX75M\namoqLVq0UN7cjHLnvpQ796XcuS/l7v+Li4tj4MCBFBcX07x5cwCCg4PZuXMnsbGx7N69m6FDh3Lo\n0CH7OQMGDGD+/PkUFBRQt25dDAYDe/bswWazkZOTQ7Vq1X7xeiUFwYEDBzh27BiPP/44WVlZrFu3\njrZt2+Ll5cXIkSNZvXo1FStWdDrPnXl0oZCfn8+KFSswGAxYrVYefvhhgoKCgOsr0X/v/9BERERE\nfqvbLUAubw0bNsRms/HEE0/Yj02aNIkhQ4bwzjvv0KZNGyIiIhwKhe7duzN8+HCmTZsGwAMPPECv\nXr3o0qUL3t7efPDBB07X6dmzJ3v37iUzM5M//vGPPPfcc2zfvh2A//mf/yEsLIwHH3yQ559/njNn\nzvDMM88AMG/ePIKDg8nMzMRsNmOz2ahZsyaJiYl387bcFR5dKFSvXt2+Kl5EREREPMO3337r8Lph\nw4Zs2bLF4diQIUPsf65QoQInT550eH/ixIlMnDjxF69x4zSim/33f/+3/c+ffPLJLduUZjrT/e53\n8dQjERERERG5M95vvvnmm64O4n5gs9nw9fWlcePGDvPL5P6mvLkv5c59KXfuS7lzX8qduILB5gkr\nLX6jtWvXkpmZSZUqVRg9erSrw5FSunDhAqtXryY/Px+DwcAjjzzi8FxjuX8VFxezaNEiLBYLVquV\n1q1bEx0d7eqwpJSsVivz588nMDDQPidX3MO7776Ln58fBoMBLy8vXnzxRVeHJKVUWFjIl19+yc8/\n/4zBYKBXr140bNjQ1WGJh/PoNQql1b59ezp27Mjq1atdHYrcAS8vLx577DHq1avH1atXmT9/Ps2a\nNaN27dquDk1uo0KFCgwZMgRfX1+sVisLFiwgKChI/9NzEzt37qR27dpcvXrV1aHIHTIYDAwdOpRK\nlSq5OhS5Qxs3biQ4OJj+/ftjsVi4du2aq0OS3wGtUeD683P1H033ExAQQL169QCoWLEitWrV4tKl\nSy6OSkrL19cXuD66YLVaMRgMLo5ISuPChQtkZWXxyCOPuDoUKSNNJHA/hYWFHD16lA4dOgDXN5Xz\n8/NzcVTye6ARBfEIeXl5nDx5kgYNGrg6FCmlkukr586do2PHjsqdm9i8eTPdunXTaIIbW7JkCV5e\nXoSEhBASEuLqcKQUzp8/T+XKlVmzZg0nT56kfv369OjRwyN3oJb7iwoFcXtXr15l5cqV9OjRQwu8\n3EjJBjWFhYWsWLGCn3/+mTp16rg6LPkVJWu56tWrx+HDh10djpTBiBEjCAgIID8/nyVLllCrVi0a\nNWrk6rDkNqxWKydOnODxxx+nQYMGbNy4kW3btmEymVwdmng4FQri1iwWCytXrqRdu3a0bNnS1eFI\nGfj5+dGkSROys7NVKNznjh49ysGDB8nKyqK4uJirV6+SlJREnz59XB2alFJAQAAAVapUoVWrVhw7\ndkyFghso2SS2ZOS1devWTvsIiNwNKhT+j+Zsuqe1a9dSu3ZtPe3IzeTn59vn2F67do1Dhw4RERHh\n6rDkNmJiYoiJiQHgyJEjbN++XUWCGykqKsJms1GxYkWKioo4dOgQUVFRrg5LSsHf35+qVaty5swZ\natWqxeHDh/XgDrknVCgAiYmJHDlyhIKCAmbPno3JZLIvGJL719GjR/nhhx+oU3eQoPoAAALiSURB\nVKcOf/vb3wDo2rUrwcHBLo5Mbufy5cusXr0am82GzWajTZs2NG/e3NVhiXi0/Px8VqxYgcFgwGq1\n8vDDDxMUFOTqsKSUevToQVJSEhaLherVq/PUU0+5OiT5HdA+CiIiIiIi4kSPRxUREREREScqFERE\nRERExIkKBRERERERcaJCQUREREREnKhQEBERERERJyoURERERETEiQoFERERERFxokJBRERERESc\nqFAQEREREREnKhRERERERMSJCgUREREREXGiQkFERERERJyoUBAREREREScqFERERERExIkKBRER\nERERcaJCQUREREREnKhQEBERERERJyoURERERETEiQoFERERERFxokJBRMTDeHl58eOPP7o6DBER\ncXMqFEREPIzBYHB1CCIi4gFUKIiIuInFixfz5JNP2l8HBwczYMAA++sHH3yQatWqAdC2bVsCAwNZ\ntWrVPY9TREQ8g8Fms9lcHYSIiNze4cOHCQkJ4dy5c5w4cYLOnTtjtVo5evQoP/74I2FhYZw9exYv\nLy8OHTpEkyZNXB2yiIi4sQquDkBEREqnSZMmBAQEsGfPHg4ePMhjjz3G3r17yczMZPv27RiNRntb\n/QYkIiK/lQoFERE3EhUVRXJyMtnZ2URHR1O9enVSUlLYsWMHUVFRrg5PREQ8iNYoiIi4kcjISFJS\nUti2bRtRUVFERkaSmppKWloa0dHRrg5PREQ8iNYoiIi4kaysLEJCQqhbty6ZmZlcunSJxo0bY7FY\nyMvLw2AwUL9+fZYsWUJMTIyrwxURETemEQURETcSHBxMQEAAkZGRAAQEBNCsWTMiIiLsj0V98803\nee6556hRowaJiYmuDFdERNyYRhRERERERMSJRhRERERERMSJCgUREREREXGiQkFERERERJyoUBAR\nEREREScqFERERERExIkKBRERERERcaJCQUREREREnKhQEBERERERJyoURERERETEyf8DD6f7zAXM\n/woAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11028a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285460973)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = ggplot(mtcars, aes(x='wt', y='mpg', color='name')) + geom_point()\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the ggplot shorthand `factor` to discretize continuous variables. For example, let's change `cyl` from a numerical to a categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jmj59unbs2GEyJgAAAHCSQBTyE7W0tKipqUljx45VMplULBaTdKy0J5NJw+kA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\n9P8B+x/rxuEaBMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff73650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285133253)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pro Tip\n",
    "Keep in mind that some aesthetics only work for continuous data, some only work for discrete data, and some work for both.\n",
    "\n",
    "For more on which aesthetics are best in certain situations, read the aesthetics API documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
